The attack surface of Industrial IoT (IIoT) is enlarged by the interconnected devices and systems. Although many works have facilitated advanced approaches to help industrial entities against possible cyber threats, they may overlook the rich operational context of manufacturing processes, leaving the security evaluation context-agnostic. Recognizing that cyber attacks and production activities are increasingly intertwined, this paper introduces Meta-KadaSec, a novelMeta-Knowledge-drivenadaptiveSecurity provisioning approach that embeds security context within the natural operational fabric of manufacturing environments. Our approach integrates: (1) STKG-PPO, a reinforcement learning model that leverages manufacturing contextual knowledge through Knowledge Graphs with Spatial-Temporal associations; and (2) Reptile-CMDPs, a meta-reinforcement learning approach that enables rapid adaptation across diverse manufacturing contexts with theoretical guarantees for convergence. Evaluations are driven by realistic attack vectors from the Edge-IIoTset dataset and an open source factory simulator, demonstrating that our context-embedded model STKG-PPO improves production efficiency by 52.5% and reduces convergence time by 6.7% compared to context-agnostic baselines. Furthermore, our meta-learning approach Reptile-CMDPs accelerates adaptation, achieving 95.2% higher average rewards compared to training from scratch.

Dynamic multi-robot systems face the intricate multi-agent pathfinding (MAPF) challenge as a pivotal hurdle. It has been uncovered through recent research that tackling MAPF issues can be effectively approached through reinforcement learning, offering a fully decentralized solution. Nonetheless, the escalation in the scale of the multi-robot system introduces sample inefficiency, posing a significant barrier for learning-based methods. We introduce a novel hierarchical reinforcement learning architecture aimed at addressing large-scale MAPF by leveraging spatial and temporal abstraction. This approach enhances exploration efficiency by recognizing intermediate rewards. The framework employs an upper-tier controller that segments the map into linked regions, thereby streamlining the optimization of agents' paths on a regional basis to foster improved global outcomes. To tackle each segmented problem, a subordinate-level controller is designed, which integrates heuristic directions and an inter-agent communication strategy. The merit of our methodology is confirmed by empirical experiments, showcasing advancements over prevailing methods in success rates and reduction in completion time across test scenarios of various magnitudes.

Vehicular Fog Computing (VFC) is significantly enhancing the efficiency, safety, and computational capabilities of Intelligent Transportation Systems (ITS), and the integration of Unmanned Aerial Vehicles (UAVs) further elevates these advantages by incorporating flexible and auxiliary services. This evolving UAV-integrated VFC paradigm opens new doors while presenting unique complexities within the cooperative computation framework. Foremost among the challenges, modeling the intricate dynamics of aerial-ground interactive computing networks is a significant endeavor, and the absence of a comprehensive and flexible simulation platform may impede the exploration of this field. Inspired by the pressing need for a versatile tool, this paper provides a lightweight and modular aerial-ground collaborative simulation platform, termedAirFogSim. We present the design and implementation of AirFogSim, and demonstrate its versatility with five key missions in the domain of UAV-integrated VFC. A multifaceted use case is carried out to validate AirFogSim's effectiveness, encompassing several integral aspects of the proposed AirFogSim, including UAV trajectory, task offloading, resource allocation, and blockchain. In general, AirFogSim is envisioned to set a new precedent in the UAV-integrated VFC simulation, bridge the gap between theoretical design and practical validation, and pave the way for future intelligent transportation domains. Our code will be available athttps://github.com/ZhiweiWei-NAMI/AirFogSim.

In recent years, there has been significant progress in semantic communication systems empowered by deep learning. It has greatly improved the efficiency of information transmission. Nevertheless, traditional semantic communication models still face challenges, particularly due to their single-task and single-modal orientation. Many of these models are designed for specific tasks, which results in limitations when applied to multi-task communication systems. Moreover, these models often overlook the correlations among different modal data in multi-modal tasks. It leads to an incomplete understanding of complex information, causing increased communication payload and diminished performance. To address these limitations, Synesthesia of Machines (SoM) provides an effective framework for fusing multi-modal data, capturing their complementary relationships. Inspired by SoM, we propose a SoM-enabled multi-task semantic communication (SoMMSC) framework. In contrast to traditional semantic communication approaches, SoMMSC can effectively handle various tasks across multiple modalities. Furthermore, we design a fusion module based on Bidirectional Encoder Representations from Transformers (BERT) for multi-modal fusion. By leveraging the powerful semantic understanding capabilities and self-attention mechanism of BERT, we achieve effective fusion of different modalities. We compare our model with multiple benchmarks. Simulation results show that SoMMSC outperforms these models in terms of both performance and communication payload.

Integrated sensing and communications (ISAC) has become increasingly crucial in next-generation wireless networks. Leveraging the reliable line-of-sight (LoS) links and mobility of unmanned aerial vehicles (UAVs), UAV-assisted ISAC has attracted significant attention. Different from the previous UAV-ISAC scenarios with single target or overlapping users and targets, we investigate ISAC in a more general multi-UAV network with independent multiple communication users and multiple sensing targets, where the UAVs provide downlink communications to the users while sensing the targets. Additionally, we consider the complicated interference management among the UAVs to further enhance the network’s practicality. Such a scenario presents a new challenge for the joint optimization problem in terms of the UAV trajectories, the user association, the target association, and the power control. Furthermore, since the existing single-objective and weighted optimization approaches may result in potential performance loss and optimization biases, we propose a dual-objective model to further optimize ISAC, aiming for a better tradeoff between the communication and sensing performance. Specifically, we propose an efficient sensing and communication dual-objective multi-UAV optimization algorithm (SC-DO-MUOA) to maximize communication rate and simultaneously minimize sensing Cramér-Rao bound (CRB). Simulation results demonstrate that our proposed SC-DO-MUOA outperforms various baselines in both communication and sensing performance.

To support cooperative perception (CP) of networked mobile agents in dynamic scenarios, the efficient and robust transmission of sensory data is a critical challenge. Deep learning-based joint source-channel coding (JSCC) has demonstrated promising results for image transmission under adverse channel conditions, outperforming traditional rule-based codecs. While recent works have explored to combine JSCC with the widely adopted multiple-input multiple-output (MIMO) technology, these approaches are still limited to the discrete-time analog transmission (DTAT) model and simple tasks. Given the limited performance of existing MIMO JSCC schemes in supporting complex CP tasks for networked mobile agents with digital MIMO communication systems, this paper presents a Synesthesia of Machines (SoM)-based task-driven MIMO system for image transmission, referred to as SoM-MIMO. By leveraging the structural properties of the feature pyramid for perceptual tasks and the channel properties of the closed-loop MIMO communication system, SoM-MIMO enables efficient and robust digital MIMO transmission of images. Experimental results have shown that compared with two JSCC baseline schemes, our approach achieves average mAP improvements of 6.30 and 10.48 across all SNR levels, while maintaining identical communication overhead.

Wireless image transmission underpins diverse networked intelligent services and becomes an increasingly critical issue. Existing works have shown that deep learning-based joint source-channel coding (JSCC) is an effective framework to balance image transmission fidelity and data overhead. However, these studies oversimplify the communication system as a mere pipeline with noise, failing to account for the time-varying dynamics of wireless channels and concrete physical-layer transmission process. To address these limitations, we propose a Synesthesia of Machines (SoM)-empowered Dynamic Channel Adaptive Transmission (DCAT) scheme, designed for practical implementation in real communication scenarios. Building upon the Swin Transformer backbone, our DCAT demonstrates robust adaptability to time-selective fading and channel aging effects by effectively utilizing the physical-layer transmission characteristics of wireless channels. Comprehensive experimental results confirm that DCAT consistently achieves superior performance compared with baseline approaches across all conditions. Furthermore, our neural network architecture exhibits high scalability due to its interpretable design, offering substantial potential for cost-efficient deployment in practical applications.

In recent years, joint radar and communication (JRC) systems have garnered significant attention due to their enhanced equipment utilization and high spectrum efficiency. This paper investigates a JRC-enabled multi-UAV cooperative system, where multiple UAVs concurrently execute communication tasks for communication users (CUs) and perception tasks for sensed targets (STs) distributed across a specified region. To strike the trade-off between the communication performance and sensing accuracy, we formulate a weighted performance optimization problem aimed at simultaneously maximizing the data transmission for CUs and minimizing the squared position error bound (SPEB) for STs, by jointly optimizing user association and channel assignment, power allocation, as well as UAV deployment. To effectively address this challenging problem, we initially recast the non-differentiable objective function into a more tractable and interpretable form with the aid of smooth approximation techniques. Subsequently, by virtue of the specific problem structure, we decompose the original joint optimization problem and develop an iterative method to optimize each subproblem sequentially. Extensive simulations demonstrate the significant performance gains of the proposed design compared to other benchmark schemes.

In cellular networks, cell handover refers to the process where a device switches from one base station to another, and this mechanism is crucial for balancing the load among different cells. Traditionally, engineers would manually adjust parameters based on experience. However, the explosive growth in the number of cells has rendered manual tuning impractical. Existing research tends to overlook critical engineering details in order to simplify handover problems. In this paper, we classify cell handover into three types, and jointly model their mutual influence. To achieve load balancing, we propose a multi-agent-reinforcement-learning (MARL)-based scheme to automatically optimize the parameters. Experimental results show that our proposed scheme outperforms existing benchmarks in balancing load and improving network performance.

In recent years, there has been significant progress in semantic communication systems empowered by deep learning. It has greatly improved the efficiency of information transmission. Nevertheless, traditional semantic communication models still face challenges, particularly due to their single-modal orientation. These models often overlook the correlations among different modal data in multi-modal tasks. It leads to an incomplete understanding of complex information, causing increased communication overhead and diminished performance. To address these limitations, Synesthesia of Machines (SoM) provides an effective framework for fusing multi-modal data, capturing their complementary relationships. Inspired by SoM, we propose a SoM-enabled semantic communication (SoMSC) framework. In contrast to traditional semantic communication approaches, SoMSC can effectively handle tasks across multiple modalities. We design a fusion module based on Bidirectional Encoder Representations from Transformers (BERT) for multi-modal fusion. By leveraging the powerful semantic understanding capabilities and self-attention mechanism of BERT, we achieve effective fusion of different modalities. We compare our model with multiple benchmarks. Simulation results show that SoMSC outperforms these models in terms of both performance and communication overhead.

The multi-drone-truck collaborative delivery, where unmanned trucks serve as mobile supply stations for drones, effectively combines the strengths of both vehicles and presents wide application prospects. But the majority of existing literature restricts drone launch and retrieve operations (LARO) to stationary trucks, and potential drone route collisions are mostly ignored. This leads to inability to fully exploit the capability of drones. We address these gaps and introduce a new variant of multi-drone-truck collaborative delivery. However, the scheduling for drones and truck faces high-dimensional solution space and complex constraints, making it almost impossible for centralized solving. To this end, we develop a hierarchical solution framework that decomposes the complete problem into two levels of subproblem. The upper solver centrally allocates tasks and schedules when drones to launch, while the lower solver, based on multi-agent reinforcement learning (MARL), plans paths for each drone agent in a decentralized but cooperative manner. In addition, we validate the effectiveness of our method by benchmarking it against three state-of-the-art approaches, demonstrating its superiority in terms of both efficiency and collision avoidance.

The highly dynamic Low Earth Orbit (LEO) environment poses significant challenges for efficient and heterogeneous service provision. A promising approach is to use the communication links of LEO satellites as signals of opportunity, where opportunistic offloading techniques appear to improve the overall performance. However, current solutions often overlook the sophisticated predictive capabilities to exploit spatio-temporal correlation across multiple dimensions (e.g., link quality, node capacity, task requirements), failing to evaluate the quality of transient opportunities for offloading decisions. This paper proposes a Spatio-Temporal Transformer-based Opportunistic Task Offloading (STOTO) approach for heterogeneous tasks in dynamic LEO. The core of STOTO is a spatio-temporal Transformer prediction model, designed to achieve superior awareness of evolving service requirements and resource availability. Then, the scheme dynamically evaluates the situation and resource availability, allocating tasks to the optimal nodes in real time. Experimental results demonstrate that our approach significantly outperforms existing methods, achieving 8.4% higher task completion rates on average.

Achieving advanced UAV autonomy is increasingly pivotal in complex low-altitude environments, while developing such AI-driven autonomous capabilities critically depend on benchmarking suites. However, existing solutions often address fundamental, isolated domains (e.g., network-centric, traffic-centric), failing to capture the intricate, comprehensive mission-level interactions in low-altitude operations. This paper introduces AirFogSim, a high-fidelity simulation platform for validating UAV autonomy in low-altitude missions. The cores of AirFogSim are a novel workflow-agent-state (WAS) modeling approach and standardized APIs for external data sources, empowering AirFogSim to simulate diverse UAV behaviors under realistic, dynamic environmental conditions. To demonstrate the practical utility, we generate a benchmark dataset for UAV situation awareness with varied weather and electromagnetic interference conditions. Multiple AI techniques are implemented and evaluated on this dataset, including traditional machine learning algorithms and large language models (LLMs). The platform is currently open-sourced on GitHub.

Modern satellite networks are transitioning from monolithic designs to microservice architectures, introducing complex spatio-temporal patterns, varied on-board processing capabilities, and high mobility with dynamic topologies. These features require accurate prediction of inter service dependencies for optimal resource allocation and system management. To address these challenges, this paper introduces the Spatio-Temporal Graph Neural Network with Orbital contextual features (STGNN-O). This model incorporates orbital information as contextual features, processes spatial dependencies through multi-head graph attention networks, and captures temporal patterns at three different timescales to complete satellite service performence metrics prediction, which refers to forecasting key performance indicators of microservices running on satellite platforms. Also, due to the lack of real-world relavent dataset, a comprehensive satellite service benchmark dataset is created based on real-world parameters and service patterns across multiple orbital configurations. Experiments demonstrate that STGNN-O significantly outperforms state-of-the-art baselines, achieving substantial improvements in prediction accuracy. Ablation studies confirm that the integration of orbital information and multi-scale temporal features significantly contributes to prediction accuracy across all orbital regimes.

ABSTRACT Intelligent vehicles require extensive data processing to enhance safety and improve driver comfort. With limited onboard computing resources, these vehicles often offload tasks to nearby vehicles or servers for auxiliary processing to meet real‐time response requirements. However, the complexity and highly dynamic nature of the vehicular environment render the design of effective offloading strategies. While existing approaches can adapt to changes in environmental parameters within vehicular networks, they are fundamentally limited by their inability to process variable‐dimensional environmental information and make decisions that scale with network size. Traditional methods typically rely on fixed‐size input representations and static computational frameworks, which are inherently unsuitable for the dynamic and scalable nature of real‐world vehicular networks that require adaptive responses to varying network sizes. As a result, existing alternatives lack feasibility to highly dynamic real‐world vehicle networks that require adaptive responses to varying network sizes. To alleviate this limitation, we develop an original approach to address the task offloading and resource allocation problem with a scalable size, via a framework based on a graph neural network (GNN). Leveraging its neighbour aggregation mechanism, GNN effectively adapts to varying‐scale topologies in dynamic vehicular networks, ensuring robust performance regardless of network size. To evaluate our proposed approach, we conducted extensive simulations to analyse its performance. The experimental results demonstrate that our method provides a more scalable and real‐time capable solution, surpassing existing approaches by seamlessly handling dynamic network size variations.

Diabetic retinopathy (DR) is a debilitating ocular complication demanding timely intervention and treatment. The rapid evolution of deep learning (DL) has notably enhanced the efficiency of conventional manual diagnosis. However, the scarcity of existing DR datasets hinders the progress of data-driven DL models, especially for pixel-level lesion annotation datasets, which severely impedes the advancement of DR lesion segmentation tasks required for precise interpretations of DR grading. Furthermore, the escalating concerns surrounding medical data security and privacy induce data collection challenges for traditional centralized learning, exacerbating the issue of data silos. Federated learning (FL) emerges as a privacy-preserving distributed learning paradigm. Nevertheless, the existing literature lacks a comprehensive FL framework for DR diagnosis and fails to exploit multiple diverse DR datasets simultaneously. To address the challenges of data scarcity and privacy, we construct a high-quality pixel-level DR lesion annotation dataset (TJDR) and propose a novel FL-based DR diagnosis framework including both DR grading and multi-lesion segmentation. Moreover, to tackle the scarcity of pixel-level DR lesion datasets, we propose$\bm {\alpha }$-Fed and adaptive-$\bm {\alpha }$-Fed, two efficient cross-dataset FL algorithms. Extensive experiments demonstrate the effectiveness of our proposed framework and the two cross-dataset FL algorithms.

Trajectory clustering is a cornerstone task in the field of trajectory mining. With the proliferation of deep learning, deep trajectory clustering has been widely researched to mine mobility patterns from massive unlabeled trajectories. Nevertheless, existing methods mostly ignore trajectories' temporal regularities, which are essential for mining fine-grained mobility patterns for applications including traveling group identification, transportation mode discovering, social security emergency, etc. To fill this gap, we propose ConDTC, a contrastive deep trajectory clustering method targeting for fine-grained mobility pattern mining. Specifically, we first design a spatial-temporal trajectory representation learning method which can capture both spatial and temporal regularities of trajectories synchronously. The proposed trajectory representation model can be used as a pre-trained model to serve various downstream trajectory mining tasks. Then, we construct a contrastive trajectory clustering module which optimizes trajectory representations and clustering performance simultaneously. Experimental results on three datasets validate that ConDTC can identify fine-grained mobility patterns by clustering trajectories with similar spatial-temporal mobility patterns together while separating those with different mobility patterns apart. Actually, ConDTC outperforms all state-of-the-art competitors substantially in terms of effectiveness, efficiency and robustness.

The evolving Industrial Internet of Things (IIoT) is shifting towards decentralized collaborative manufacturing, posing heightened network security issues within interconnected value chains, thus requiring advanced Network Intrusion Detection (NID) systems to identify potential threats. In this context, traditional centralized NID systems are insufficient due to cross-industrial privacy concerns and interconnected secure threats. Federated Learning (FL) has emerged as a promising solution to enable the sharing of security insights without compromising privacy across participants. However, establishing an FL-based NID framework in realistic IIoT scenarios faces several hurdles, including the limited availability of large-scale devices and heterogeneous attack data distributions. The former leads to inconsistent client participation and degraded performance, while the latter hinders model convergence. To address these, we propose a novel Federated Learning-based Industrial Network Intrusion Detection (FedIn-NID) framework, incorporating a multidimensional client selection strategy and a dynamic global aggregation strategy. The selection strategy synergistically considers multidimensional factors including client availability, local dataset distribution, and dataset size. This approach accommodates clients with varying availability and avoids the selection of biased clients with data concentrated in a few categories. During model aggregation, the proposed strategy leverages the concept of exponential moving average to dynamically balance the holistic yet slightly older knowledge in the global model with the partial but relatively newer knowledge in local models, ensuring effective aggregation and convergence of the global NID model. Experiments demonstrate that FedIn-NID outperforms baselines by 10% to 30%, showcasing remarkable robustness with increasing data distribution heterogeneity and device count.

Federated semi-supervised learning (FSSL) has recently emerged as a promising approach for enhancing the performance of federated learning (FL) using ubiquitous unlabeled data. However, this approach encounters challenges when learning a global model using both fully labeled and fully unlabeled clients. Previous works overlook the dissimilarities between labeled and unlabeled clients, predominantly using shared parameters for local training across these two types of clients, thereby inducing intertask interference during local training. Moreover, these works typically adopt a single-perspective aggregation strategy, primarily focusing on data-volume-aware aggregation (i.e., FedAvg), leading to a lack of comprehensive consideration in model aggregation. In this article, we propose a novel FSSL method termed $\text {D}^{{2}}\text {Fed}$ , which addresses these issues by rethinking the roles of labeled clients and unlabeled ones to mitigate intertask interference during local training and by integrating client-type-aware with data-volume-aware to provide a more comprehensive perspective for model aggregation. Specifically, in local training, our proposed $\text {D}^{{2}}\text {Fed}$ distinguishes between the primary and accessory roles of labeled and unlabeled clients, respectively, performing dual-role additive local training (DALT) accordingly. In global aggregation, $\text {D}^{{2}}\text {Fed}$ uses a dual-perspective global aggregation (DGA) strategy, transitioning from data-volume-aware aggregation to client-type-aware aggregation. The proposed method simultaneously improves both local training and global model aggregation for FSSL without compromising privacy. We demonstrate the effectiveness and robustness of the proposed method through extensive experiments and elaborate ablation studies conducted on the CIFAR-10/100, SVHN, FMNIST, and STL-10 datasets. Experimental results show that $\text {D}^{{2}}\text {Fed}$ outperforms state-of-the-arts on five datasets under diverse data settings.

Network slicing is the key enabler for the 5G Industrial Internet of Things (IIoT), allowing tailored services and security guarantees for vertical industries. With the advent of 5G-Advanced (5G-A) and 6G era, the number of slices will increase significantly, leading to more diverse security requirements given different slice features. To provide adaptive security management spanning multiple slices in IIoT, this paper proposes a novel slice-specific secure IIoT (SSIOT) architecture with an AI-enabled solution. The SSIOT architecture separates the control and data planes, where the control plane orchestrates the Security Service Function Chains (SSFC) across network slices and the data plane analyzes the slice-specific features like traffic patterns, resource SLA guarantees, and Virtual Security Network Function (VSNF) dependencies. To extract these spatial-temporal features from the dynamic IIoT environments, we facilitate the powerful deep reinforcement learning (DRL) methods and propose a structural GS2L approach. GS2L is maliciously designed with the core principles of graph convolutional network (GCN) and Gated Recurrent Unit (GRU), enabling a thorough understanding of physical resource distribution and the request dynamics across slices. Extensive experiments are conducted in diverse IIoT slices with the real-world USNet and fat-tree topologies. Simulation results demonstrate that GS2L outperforms state-of-the-art learning and heuristic benchmarks, showcasing an overall 15.2% improvement with efficient and stable resource utilization.

In this paper, we focus on the task offloading problem for zero-trust vehicular fog computing (VFC) to promote trustworthy collaborative computing among vehicles. We propose a blockchain-enabled zero-trust VFC framework (BlockZT-VFC) to continuously verify and dynamically authorize the vehicle nodes. To overcome the reliability and efficiency issues in BlockZT-VFC, a multi-attribute task offloading and group-based continuous verification (MTOCV) scheme is designed. In particular, multiple attributes are extracted from both vehicles and tasks, ensuring that tasks are offloaded to FVs with authorized trustworthiness, and the group-based continuous verification efficiently guarantees the resulting authenticity by evaluating independent probability mathematically. Experimental simulations demonstrate an 18% increase in throughput and a 34% decrease in latency across two different scenarios, showcasing the superiority of our mechanism in improving system performance and reliability.

In vehicular networks, collaborative decision-making can improve the recognition capability and driving efficiency of vehicles, while easy to suffer from false information injection from malicious nodes (MNs). Therefore, in this paper, we investigate collaborative decision-making in a hierarchical blockchain-enabled vehicular network in the presence of self-interested MNs. In order to improve the accuracy of collaborative decision-making, we propose a reputation-based decision-making approach, which consists of two parts: 1) We devise a Bayesian static game model for an attack-defense game between MNs and miners in the blockchains. By providing incentives in expected payoffs, the scheme motivates MNs to cease attacks, thereby reducing the dissemination of false information during the collaborative process. The pure-strategy Bayesian Nash equilibrium (BNE) of MN quitting attack is further studied. 2) We introduce a collaborative decision-making algorithm based on Dempster-Shafer Theory (DST) to facilitate precise decision-making in the presence of false information. This algorithm leverages the hierarchical blockchain architecture, integrating both historical and local reputation of a node to assess credibility, mitigating the influence of untrusted information. Simulation results demonstrate the effectiveness of our approach.

The Industrial Internet of Things (IIoT) continues to evolve alongside advancements in 5G and beyond 5G communication technologies, and network slicing has emerged as a promising technique to offer isolated slices and tailored services across industrial use cases, which profoundly affects the traditional security solution. As the future IIoT evolves towards the post-5G era, the anticipated growth in network slices will unavoidably exacerbate challenges in security service management, but developing an adaptive and effective security strategy remains a significant challenge. This study introduces a novel slice-specific IIoT (SSIOT) architecture, meticulously crafted to address the unique security needs of each slice based on network function virtualization. To adapt to the SSIOT, an AI-driven GS2L model is presented, which combines graph convolutional network (GCN) with sequence-to-sequence (Seq2Seq) deep reinforcement learning (DRL). The GS2L offers the network topology explanation module, service request analysis module, and slice-specific distribution extraction module, and adeptly navigates the multifaceted Security Service Function Chain (SSFC) embedding conundrum and resource allocation in slice-specific systems. Comprehensive experimental evaluations underscore GS2L's superiority, showcasing its proficiency in delivering augmented QoS satisfaction while ensuring prudent resource utilization over the learning-based and heuristic benchmark.

While autonomous intersection management (AIM) emerges to facilitate signal-free scheduling for connected and autonomous vehicles (CAVs), several challenges arise for planning secure and swift trajectories. Existing works mainly focus on addressing the challenge of multi-CAV interaction complexity. In this context, multi-agent reinforcement learning-based (MARL) methods exhibit higher scalability and efficiency compared with other traditional methods. However, current AIM methods omit discussions on the practical challenge of CAV heterogeneity. As CAVs exhibit different dynamics features and perception capabilities, it is inappropriate to adapt identical control schemes. Besides, existing MARL methods that lack heterogeneity adaptability may experience a performance decline. In response, this paper exploits MARL to model the decision-making process among CAVs and proposes a novel heterogeneous-agent attention gated trust region policy optimization (HAG-TRPO) method. The proposed method can accomplish more effective and efficient AIM with CAV discrepancies by applying a sequential update schema that boosts the algorithm adaptability for MARL tasks with agent-level heterogeneity. In addition, the proposed method utilizes the attention mechanism to intensify vehicular cognition on disordered ambience messages, as well as a gated recurrent unit for temporal comprehension on global status. Numerical experiments verify that our method results in CAVs passing at the intersection with fewer collisions and faster traffic flow, showing the superiority of our method over existing benchmarks in terms of both traffic safety and efficiency.

The rise of collaborative manufacturing, driven by the rapid proliferation of Industrial Internet of Things (IIoT) technologies, has markedly enhanced agility and productivity in industrial environments. However, this advancement has also significantly broadened the attack surface and uncovered unique vulnerabilities intrinsic to these interconnected systems. This paper introduces a novel Hierarchical Federated Incremental Learning Network Intrusion Detection (HFIN) approach. To our knowledge, this is the first attempt to address the ever-evolution network intrusion detection (NID) challenges in IIoT landscapes from the continuous attack-defense perspective. Our proposed HFIN capitalizes on decentralized model training across multifarious IIoT devices, ensuring data privacy and empowering continuous learning capabilities. It utilizes distributed data sources for secure experience sharing, collaboratively enhancing the continuous detection performance of the global model. Furthermore, regarding the inherent resource constraints of IIoT devices, we proposed a novel edge-client Weighted Transmission Optimization strategy (WTO). This strategy adeptly balances effective intrusion detection with the operational constraints of IIoT devices. By holistically considering detection capabilities and data volume across different attack types, it prioritizes the transmission of more critical and scarce attack data for training within bandwidth constraints. This maintains the comprehensive detection capability of the global model against various network attacks. To validate the effectiveness of HFIN, we conduct extensive experiments using the NF-UQ-NIDS-v2 and NF-ToN-IoT-v2 datasets. Experimental results demonstrate that our method outperforms baselines by approximately 10% in terms of accuracy and F1-score, highlighting the applicability and effectiveness of HFIN in enhancing security against sophisticated industrial environments and ever-evolving cyber threats.

In the realm of human mobility data analysis, a multitude of constraints result in the publication of sparse, non-uniform implicit trajectories without explicit location information, such as coordinates. Researchers have dedicated substantial efforts towards trajectory recovery, aiming to densify trajectories and gain a more comprehensive understanding of human mobility. However, existing trajectory recovery methods focus on explicit trajectories, and require extensive historical data to capture users' mobility patterns. Nevertheless, implicit trajectories are usually more sparse than explicit trajectories. Addressing these challenges, we propose TrajBERT, an innovative BERT-based trajectory recovery method with spatial-temporal refinement. TrajBERT employs the Transformer encoder to learn mobility patterns bi-directionally and enhances the predictions by cross-stage temporal refinement. Subsequently, we design an output layer with global spatial refinement with a novel spatial-temporal aware loss function. To evaluate the performance of TrajBERT, we conduct a series of experiments on real-world datasets. Remarkably,TrajBERT yields at least 8.2% performance improvement compared to the state-of-the-art trajectory recovery approachs. Furthermore, TrajBERT successfully mitigates the cold start problem commonly experienced with new users lacking historical trajectories. It also shows superior robustness when faced with extremely sparse trajectories, thus demonstrating its potential as a practical tool in the field of human mobility analysis.

Vehicular fog computing (VFC) has emerged as a promising solution to mitigate vehicular network computation load. In the hierarchical VFC, vehicles are employed as mobile fog nodes at the edge to provide reliable and low-latency services. Particularly, since privately-owned vehicles are rational nodes, their intentions for both computation provision and service demand should be considered instead of overestimating their willingness. To remunerate the participation intentions of vehicles as well as improve vehicular fog resource utilization in the large-scale VFC, the trading-based mechanism is a potential solution. In this article, we propose a many-to-many task offloading framework based on the vehicular trading paradigm. This framework enables computational resource trading across different VFC subsystems and decides the multi-tier task offloading results based on the trading consensus. The trading process is viewed as a partially observable Markov decision process (POMDP) and a Multi-Agent Gated actor Attention Critic (MA-GAC) approach is designed to reach an effective and stable offload-and -serve cooperation among vehicles. Theoretical analyses and experiments verify the feasibility and efficiency of the proposed framework, and simulation results demonstrate that the coordinated MA-GAC approach not only benefits vehicles with higher long-term rewards but also optimizes the system social welfare in a distributed manner.

Unmanned aerial vehicle (UAV) communications have emerged as a promising solution for future full coverage networks. To further meet the massive connection demands in beyond-fifth-generation (B5G) systems, in this paper, we propose to employ non-orthogonal multiple access (NOMA) in both uplink and downlink relaying hops in an amplify-and-forward (AF) based UAV relaying network with multiple source-destination (SD) user pairs. Specifically, taking NOMA design in both hops into a joint consideration in relaying networks is investigated for the first time and presents a new challenge for the joint optimization problem in terms of the deployment of the UAV relay, the two-hop NOMA user grouping, and the transmit power control for both the source users and the UAV. To maximize the system sum rate, we propose an efficient joint uplink and downlink NOMA-based relay (JUDNR) scheme to decompose the problem into three sub-problems and adopt the alternating optimization (AO) method to iteratively obtain a promising solution. In detail, we provide a joint NOMA groups design of both the uplink and downlink and propose a novel recursive two-hop NOMA grouping (RTNG) algorithm to efficiently group users. Simulation results demonstrate that our proposed JUDNR scheme outperforms various baselines in terms of sum rate.

Vehicular fog computing (VFC) has been expected as a promising architecture that can make full use of computing resources of idle vehicles to increase computing capability. However, most current VFC architectures only focus on the local region and ignore the spatio-temporal heterogeneity of computing resources, resulting in that some regions have idle computing resources while others cannot satisfy the requirements of tasks. To further improve the overall computing resource utilization in the whole network, in this work, we propose a hierarchical VFC architecture, where neighboring regions can share their idle computing resources. Considering the high complexity of both inter- and intra-region cooperative task offloading in such a hierarchical VFC architecture, we put forward a distributed task offloading strategy based on multi-agent reinforcement learning in which the multi-agent reinforcement learning method is designed to learn each task vehicle’s offloading strategy in a distributed manner. Moreover, to tackle the inefficiency caused by the multi-agent credit assignment problem, we provide the counterfactual multi-agent reinforcement learning approach which exploits a counterfactual baseline to evaluate the action of each agent. Simulation results validate that the proposed hierarchical VFC architecture can effectively improve the global task computing efficiency and the proposed mechanism outperforms the baseline algorithms.

Unmanned aerial vehicle (UAV) swarm-based sensing technology has become increasingly important due to its exceptional maneuverability, versatile coverage capabilities, and reliable line-of-sight (LoS) connectivity. However, the sensing accuracy improvement by exploiting resource coordination strategy poses a new challenge on multi-UAV sensing system. In this paper, we consider the problem of cooperative sensing via a system of multi-UAV, where each UAV is equipped with a directional antenna to cooperatively conduct energy detection for several targets of interest. To measure the perception ability of the system, we choose the energy detection probability as the metric, aiming to maximize the sum detection probability of the network by jointly optimizing UAVs’ deployment, as well as the directional antenna orientations. By virtue of the specific problem structure, we recast the formulation into an equivalent yet more tractable form with the aid of auxiliary vectors. Subsequently, we propose an efficient iterative algorithm for the solution based on the alternating direction penalty method (ADPM), which decomposes the formulated non-convex problem into multiple subproblems and solves them alternately. Extensive simulations validate the efficacy of the proposed algorithm and provide valuable insights for practical system design.

Unmanned aerial vehicle (UAV) relays are increasingly playing a significant role with their advantages of quick establishment for emergency communication, expansion of communication coverage, and increase of system capacity. As demand for massive simultaneous connections is ever-increasing, UAV relaying communication aided by non-orthogonal multiple access (NOMA) can more efficiently utilize spectrum resources to further improve capacity. To fully unlock the potential of NOMA in complex and practical relaying networks, we investigate a pioneering two-hope NOMA UAV relaying network with multi-pair separated users, where the uplink and downlink resources are jointly considered. For the purpose of enhancing the efficiency of our challenging UAV relaying, a developed joint uplink and downlink NOMA relay (JUDNR) scheme is proposed, which jointly optimizes UAV location, user grouping, and power control. Specifically, based on alternating-optimization (AO) method, we iteratively solve non-convex sub-problems by successive-convex-approximation (SCA) algorithm, a novel recursive reformulation grouping (RRG) algorithm based on fractional programming, and difference-of-convex (DC) algorithm, respectively. Simulation results demonstrate that our scheme can significantly enhance the system sum rate performance compared with reference strategies.

The Multi-Agent Path Finding (MAPF) problem is a critical challenge in dynamic multi-robot systems. Recent studies have revealed that multi-agent reinforcement learning (MARL) is a promising approach to solving MAPF problems in a fully decentralized manner. However, as the size of the multi-robot system increases, sample inefficiency becomes a major impediment to learning-based methods. This paper presents a hierarchical reinforcement learning (HRL) framework for large-scale multi-agent path finding, featuring applying spatial and temporal abstraction to capture intermediate reward and thus encourage efficient exploration. Specifically, we introduce a meta controller that partitions the map into interconnected regions and optimizes agents' region-wise paths towards globally better solutions. Additionally, we design a lower-level controller that efficiently solves each sub-problem by incorporating heuristic guidance and an inter-agent communication mechanism with RL-based policies. Our empirical results on test instances of various scales demonstrate that our method outperforms existing approaches in terms of both success rate and makespan.

Vehicular fog computing (VFC) can make full use of computing resources of idle vehicles to increase computing capability. However, most current VFC architectures only focus on the local region and ignore the spatio-temporal distribution of computing resources, resulting that some regions have idle computing resources while others cannot satisfy the requirements of tasks. Therefore, we propose a hierarchical VFC architecture, where neighboring regions can share their idle computing resources. Considering that the existing centralized offloading mode is not scalable enough and the high complexity of cooperative task offloading, we put forward a distributed task offloading strategy based on multi-agent reinforcement learning. Moreover, to tackle the inefficiency caused by the multi-agent credit assignment problem, we provide the counterfactual multi-agent reinforcement learning approach which exploits a counterfactual baseline to evaluate the action of each agent. Simulation results validate that the hierarchical architecture and the distributed algorithm improves the efficiency of global performance.

Trajectory clustering is a cornerstone task in trajectory mining. Sparse and noisy trajectories like Call Detail Records (CDR) have become popular with the rapid development of mobile applications. However, existing trajectory clustering methods' performance is limited on these trajectories. Therefore, we propose a dual Self-supervised Deep Trajectory Clustering (SDTC) method, to optimize trajectory representation and clustering jointly. First, we leverage the BERT model to learn spatial-temporal mobility patterns and incorporate them into the embeddings of location IDs. Second, we fine-tune the BERT model to learn cluster-friendly representations of trajectories by designing a dual self-supervised cluster layer, which improves the intra-cluster similarities and inter-cluster dissimilarities. Third, we conduct extensive experiments with two real-world datasets. Results show that SDTC improves the clustering accuracy by 12.1% (on a noisy and sparse dataset) and 3.8% (on a very sparse dataset) compared with SOTA deep clustering methods.

In this paper, we consider the problem of cooperative sensing via a system of multi-unmanned aerial vehicles (UAVs), where each UAV is equipped with a directional antenna to cooperatively perform detection tasks for several targets of interest. To measure the perception ability of the system, we choose the detection probability as the metric, aiming to maximize the sum detection probability of targets by jointly optimizing UAVs’ deployment and directional antenna orientations. To tackle the inherent nonconvexity of the formulated problem, we first decompose it into two sub-problems, i.e., a slave problem for optimizing the antenna orientations with a given UAVs’ deployment, and a master problem for optimizing the UAVs’ deployment. By virtue of the slave problem structure, an efficient block coordinate descent (BCD) algorithm is developed. Meanwhile, to deal with the lack of the closed expression of the sum detection probability with respect to the UAVs’ deployment, we further develop an iterative algorithm to acquire an efficient solution with the aid of Gibbs Sampling (GS) approach. Extensive simulations demonstrate the efficacy of the proposed algorithm.

Abstract While the terrestrial base stations (TBSs) in the fifth‐generation (5G) network provide high throughput for the conventional terrestrial users (TUs), it is still challenging for the network to support massive TUs and unmanned aerial vehicles (UAVs) simultaneously due to the complicated air–ground channel and severe interference. In this paper, the deployment of a high‐altitude platform (HAP) as a supplement for the terrestrial networks, in which the HAP and TBSs serve TUs and UAVs simultaneously in a joint manner, is studied. The novel network has two challenges. First, the deployment of the HAP, which is a new degree of freedom, should be optimized considering the terrestrial network. Second, the channel of the joint HAP and TBS network that serves multiple TUs and UAVs concurrently is complicated, and the resource allocation of the network should be designed. To tackle the above two challenges, a joint resource allocation and HAP deployment problem are formulated, and a gradient‐and‐matching‐based algorithm is proposed to solve it efficiently. Simulation results show that the HAP and the proposed algorithm enhance the sum‐rate of the network by over 30%, and the average data rate of both the TUs and the UAVs can be effectively improved.

Electric vehicles (EVs), one of the most effective solutions to reduce gas emission and realize fossil fuels replacement, are enjoying growing popularity from governments to customers. The development of EVs leads to significant advances in vehicle automation and electrification, but meanwhile poses additional heavy charging and data processing burden on current smart grid. Considering the mutual demand and supply relationship between EVs and smart grid in both charging and computing tasks, we integrate vehicular fog computing (VFC) and smart EV charging for joint optimization and propose an integrated charging and computing (IC2) architecture for EV-included smart grid. In the proposed IC2 architecture, charging stations are profit-driven third-party power prosumers that also help compute tasks offloaded by smart grid while EVs act as both energy consumers and computation providers. We employ the contract theory to provide a multiattribute contract-based charging protocol for EVs and charging stations in an information asymmetry scenario. To obtain the optimal contract, we derive KKT conditions and design a convex–concave-procedure-based contract optimization algorithm. We also design a heuristic offloading algorithm to assign heterogeneous tasks toward different EVs. Numerical results indicate that the proposed multiattribute contract-based charging-computing scheme can effectively benefit both the charging stations and EVs, and meanwhile improves the task computation capability in EV-integrated smart grid.

Vehicular fog computing (VFC) is regarded as a promising framework for vehicular computing applications by utilizing local spare resources of nearby vehicles to conduct ubiquitous time-critical and data-intensive tasks. Meanwhile, how to provide stable and low-latency services through real-time task offloading has become a heated issue. Opposite to the traditional task-to-individual offloading manner, in this article, we propose a novel task-block (TB)-based offloading paradigm for VFC, in which the tasks are merged into blocks to be assigned and offloaded. This TB-based offloading paradigm effectively alleviates the offloading decision-making burden and, thus, reduces the overall computation latency in the dynamic vehicular environment. Faced with transmission-reliable and time-intensive requirements of TBs, we turn to cooperation among vehicles and further propose a TB-based overlapping matching-coalition (TBOMC) scheme integrating overlapping coalition formation (OCF) game with matching theory to address the complicated offloading problem. The OCF game framework encourages vehicular fog nodes to devote their resources and form collaborative computing groups in a distributed method. Numerical results demonstrate that the TBOMC scheme better exploits local computing capabilities and outperforms from 5% to 12% over other existing benchmarks.

With increasing time-critical and computation-intensive tasks generated by mobile applications, vehicular fog computing (VFC) has emerged as a promising solution to relieve the overload on roadside units (RSUs) or cloud centers. In VFC, tasks are offloaded to vehicular fog nodes local to the client devices, which exploits the under-explored computational resources of nearby vehicles. In this paper, we propose a novel cooperative vehicular fog computing architecture from an overlapping perspective, termed Overlapping-enabled Cooperative Vehicular fog Computing (OCVC) to fully utilize vehicular fog nodes' local potential resources. Different from traditional cooperative VFC architecture where each vehicle only works in one fog computing group at one time, the proposed OCVC architecture enables vehicles to participate in different computing groups simultaneously, and thus is able to fully exploit potential computational resources in an overlapping manner. In addition, we provide a distributed OCVC scheme to solve the complicated computing group formation, overlapping resource allocation, and task assignment problem by employing the overlapping coalition formation (OCF) game framework and a heuristic offloading algorithm. We conduct simulations for performance comparison in terms of diversified performance metrics and numerical results show that the proposed OCVC scheme performs better than other benchmarks under different conditions.

5G communication network has become a major pillar in the evolution of interconnected industrial systems. However, the introduction of 5G network may lead to unknown risks in the systems. To reveal the impact of network threats on 5G-based industrial systems, a 5G network security analysis approach combining formal modeling and attack penetration is proposed. Firstly, the 5G network models based on topology and transmission events are established to cope with diverse and hidden attack routes and behaviors. Then, the attack module is integrated into the network model. With attack penetration to the models, potential vulnerabilities are exploited and quantified based on the hierarchical-topology model, and network reliability is evaluated based on the transmission-event model. The simulation results identify and quantify network vulnerabilities under various attacks, including access authentication failure, destruction of data integrity, illegal control of Network Functions (NFs), and malicious consumption of shared slicing resources. Meanwhile, a more unpredictable outcome is that there is a threshold of access probability,$\alpha $, to measure the impacts of attacks against the bearer network and core network on reliability. Finally, a practical case about the impact of network security on a 5G-based coupled-tank system is discussed, which further proves the feasibility of our approach.

In this paper, we consider an uplink Internet of Things (IoT) network consisting of time-sensitive devices and throughput-sensitive devices, focusing on effective coordination between different performance requirements. To improve the spectral efficiency, Non-Orthogonal Multiple Access (NOMA) is introduced to obtain a potential performance gain by assigning the spectrum resources to different types of devices with diverse performance requirements in a coordinative and resource-sharing manner. Considering the performance diversity of heterogeneous devices, we provide two NOMA user grouping strategies: Mixed Block Strategy (MBS) and Uniform Block Strategy (UBS) which configure NOMA user groups as those consisting of different types of devices and those containing only a single type of device, respectively. Then, we propose a hybrid resource allocation protocol by switching between these two strategies. The performance of a special case with two time-sensitive devices and two throughput-sensitive devices is first optimized to evaluate the proposed strategies, and then extended to compare with traditional strategy in the multi-user scenarios. Simulation results illustrate that MBS and UBS outperform the traditional strategy, i.e., lower Age of Information (AoI) and higher throughput, and have their own advantages at different system parameters. On the basis, the hybrid protocol can further improve the system performance.

Leaking oil from a stuffing box (packing) during the process of oil extraction may lead to serious economic as well as environmental problems. In recent years, a number of deep learning based methods have been proposed to build a real-time oil leakage detection system by analyzing data collected from different sensors. However, deep learning is a data hungry technology. To train a leakage detection model with satisfactory accuracy, numerous diversified training data are needed, which is hard to be collected by a single enterprise. Building collaboration among different enterprises by sharing sensor data for training can yield a better oil leakage detection model. But such collaboration is usually limited by concerns on sensitive information contained in sensor data being leaked. To handle these concerns and exploit benefits from collaboration training, in this paper, we leverage federated learning (FL), which is an emerging decentralized deep learning paradigm, to build an oil leakage detection model in a collaborative but privacy-protecting manner. In addition, considering data across enterprises may be labeled in different manners, we also carefully design an FL personalized strategy so that our collaboration paradigm can be conducted on data with label shift problems. We evaluate our methods on a real-world industry dataset and demonstrate that 1) Models yielded by our FL system can achieve very competitive results compared with models yielded by data-sharing collaboration; 2) Our proposed personalized strategy can properly handle the label shift problems under traditional FL setup.

With the ever-increasing traffic congestion issues and fast development of autonomous and connected vehicles, autonomous intersection management (AIM) has been recently proposed as a promising concept to effectively and safely enhance the traffic efficiency at intersections. However, most current literature focus on discrete space modeling, and there is few investigation on continuous space modeling in AIM due to high computational complexity, though continuous modeling can lead to better accuracy and performance. In this paper, we propose a novel multi-agent reinforcement learning-based AIM protocol with continuous intersection modeling and action space. To address the challenge of high data dimensionality and computational complexity in continuous modeling, we further provide an attention mechanism in our learning-based AIM protocol, termed as self-attention proximal policy optimization (SA-PPO) algorithm. The proposed SA-PPO algorithm can make a vehicle to control the speed more precisely and extract relevant information from neighboring vehicles to reduce data complexity. Results demonstrate that our proposed protocol can improve the traffic performance by up to 30% compared with commonly used DQN algorithm in complex and dense traffic environments.

Electric vehicles (EVs) are enjoying growing popularity from governments to customers. However, the development of EVs inevitably poses heavy charging and data processing burden on current smart grid. Considering the mutual demand and supply relationship between EVs and smart grid in both charging and computing tasks, we integrate vehicular fog computing and EV charging for joint optimization and propose an integrated charging-computing

Confronted with the increasing computation-intensive requirements of vehicular applications, vehicular fog computing (VFC) has emerged as the promising solution to mitigate the load at the edge of vehicular network. In VFC, vehicles are employed as vehicular fog nodes to provide reliable services with applicability. However, considering the individual serving and offloading intentions of the privately-owned vehicles, the many-to-many task offloading in dynamic vehicular environment becomes a challenging problem. In this paper, we propose a distributed dynamic many-to-many task offloading framework based on vehicle-to-vehicle (V2V) trading paradigm to improve the fog resource utilization in VFC. In order to reach an effective and stable offload-and-serve cooperation between vehicles as service demanders and vehicles as computation providers in the proposed framework, we formulate the trading process as a partially observable Markov decision processes (POMDP) and design a Multi-Agent Gated actor Attention Critic (MA-GAC) approach, leading to an efficient offloading optimization process in a distributed manner. Theoretical analysis and experiments verify the feasibility and efficiency of the proposed framework, and simulation results demonstrate that the proposed MA-GAC approach outperforms other benchmarks in the dynamic environment.

Medical image segmentation (MIS) plays a vital role in modern computer-aided diagnosis systems. Deep learning technology has achieved promising results in MIS in recent years. However, deep learning (DL) is a data-hungry technology and in the domain of healthcare, a single hospital usually cannot afford to collect adequate medical images to train a robust DL model. Moreover, a medical image usually contains sensitive data related to patients' privacy, making it's infeasible to build a larger dataset by collecting images from different hospitals. Federated learning (FL), a recently proposed privacy-protecting collaborative paradigm aiming at allowing different data owners to collaboratively train a model without exposing raw data, seems to be a proper solution to these problems. Some recent works have verified the feasibility of applying FL to MIS but most of these works only confine to fully supervised scenarios. Unfortunately, most hospitals in realistic usually cannot provide fully labeled data due to lack of labor. In this paper, we study a challenging but more practical problem in which each hospital can only provide a few labeled data combined with some other unlabeled data. To effectively handle such a problem, we propose a novel and robust federated semi-supervised learning (FSSL) framework, which improves over the mean teacher mechanism with a cross-clients ensemble module and a model-wise self-ensembling module. We evaluate our method on two public medical image datasets and the results show that, in the challenging FSSL scenario, our method can effectively leverage unlabeled data to boost the model performance by a considerable margin. Notably, our method also outperforms other existing FSSL approaches designed for MIS.

Vehicular fog computing (VFC) has emerged as a promising solution to relieve the overload in vehicular network. Since individual vehicular fog node is incapable of providing ultra-reliable and low-latency services constrained by limited resources, cooperation among vehicles becomes an attractive attempt to promote quality of service (QoS). In this paper, we propose a novel Overlapping-enabled Cooperative Vehicular Computing architecture in VFC, termed OCVC, to fully utilize vehicular fog nodes' local potential resources. The proposed OCVC architecture enables vehicles to participate in different fog groups simultaneously different from traditional cooperative computing architecture. In addition, we propose a distributed OCVC scheme to solve the complicated computing group for-mation, overlapping resource allocation, and task assignment problem based on overlapping coalition formation (OCF) game framework. We conduct experiments in several metrics and numerical results show that the proposed OCVC scheme per-forms at least 5 % better than other benchmarks under different conditions.

Unmanned aerial vehicle (UAV) has been more and more widely used in military and civilian, with its unique advantages of flexibility, convenience, and wide coverage. As flying stations, UAVs can quickly set up relay communication links for different missions, to enhance the receiving signal power, increase the system capacity, and expand the communication coverage. In this article, we investigate a two-way multihop UAV relaying network, where there are two ground users as sources and multiple UAVs as relays to help the two ground sources exchange information. For the purpose of enhancing the efficiency of the investigated UAV-assisted relaying, we come up with a productive two-way multihop UAV relaying pattern, which can achieve a data rate of$({1}/{2})$data packets per time slot with the decode-and-forward protocol. Then, we further formulate a joint transmit power and trajectory optimization problem for the UAVs in this two-way multihop relaying scenario. The formulated problem is nonconvex which makes it difficult to solve directly; hence, we propose an iterative algorithm to obtain an approximate optimal solution based on block coordinate descent and successive convex optimization techniques. Numerical results demonstrate that our proposed two-way multihop UAV relaying network achieves significant throughput gains compared with other benchmark schemes.

In recent years, traffic flow prediction has attracted more and more interest from both academia and industry since such information can provide effective guidance for traffic management or driving planning and enhance traffic safety and efficiency. But due to the complicated spatial-temporal dependence in actual roads and the limitation of intersection monitoring equipment, there are still many challenges in spatial-temporal traffic flow prediction. In this paper, we propose a novel hierarchical traffic flow prediction protocol based on spatial-temporal graph convolutional network (ST-GCN), which incorporates both spatial and temporal dependence of intersection traffic to achieve a more accurate traffic flow prediction. Different from existing works, our proposed protocol with the Adjacent-Similar algorithm can also effectively predict the traffic flow of the intersections without historical data. Experiments based on practical traffic data of the city of Qingdao, China demonstrate that our proposed ST-GCN-based traffic flow prediction protocol outperforms the state-of-the-art baseline models. Moreover, as for the intersections without historical data, we can also obtain a good prediction accuracy.

Accurate behavior prediction of other vehicles in the surroundings is critical for intelligent transportation systems. Common practices to reason about the future trajectory are through their historical paths. However, the impact of traffic context is ignored, which means the beneficial environment information is deserted. Although a few methods are proposed to exploit the surrounding vehicle information, they simply model the influence according to spatial relations without considering the temporal information among them. In this paper, a novel multi-vehicle collaborative learning with spatio-temporal tensor fusion model for vehicle trajectory prediction is proposed, which introduces a novel auto-encoder social convolution mechanism and a fancy recurrent social mechanism to model spatial and temporal information among multiple vehicles, respectively. Furthermore, the generative adversarial network is incorporated into our framework to handle the inherent multi-modal characteristics of the agent motion behavior. Finally, we evaluate the proposed multi-vehicle collaborative learning model on NGSIM US-101 and I-80 benchmark datasets. Experimental results demonstrate that the proposed approach outperforms the state-of-the-art for vehicle trajectory prediction. Additionally, we also present qualitative analyses of the multi-modal vehicle trajectory generation and the impacts of surrounding vehicles on trajectory prediction under various circumstances.

Hop count distribution (HCD), generally formulated as a discrete probability distribution of the hop count, constitutes an attractive tool for performance analysis and algorithm design. This paper devotes to deriving an analytical HCD expression for a finite ad hoc network under the minimum hop-count routing protocols. Formulating the node distribution with binomial point process, the network is provided as a bounded area with all nodes randomly and uniformly distributed. Considering an arbitrary pair of source node (SN) and destination node, an innovative and straightforward definition is presented for HCD. In order to derive HCD out, an original mathematical framework, named as the equivalent area replacement method (EARM), is proposed and verified. Under the EARM, HCD is derived by first considering the special case where SN locates at the network center and then extending to the general case where SN is randomly distributed. For each case, the accuracy of our HCD model is evaluated by simulation comparison. Results show that our model matches well with the simulation results over a wide range of parameters. Particularly, the derived HCD outperforms the existing formulations in terms of the Kullback Leibler divergence, especially when SN is randomly distributed.

Based on the advantages of small size, light weight, as well as flexible deployment and recycling, unmanned aerial vehicle (UAV) has been more and more widely used in military and civilian. As flying relays, UAVs can quickly set up relay communication links for different missions, to enhance the receiving signal power, increase the system capacity, and expand the communication coverage. In this paper, we investigate full-duplex (FD) UAV relaying for multiple source-destination pairs. To fully exploit the flying flexibility of the UAV in serving multiple source-destination pairs, we propose a scheduling protocol that exploits time division multiple access (TDMA) to serve different source-destination pairs in turns when flying along an optimized trajectory. Then, we further formulate a joint optimization problem of the TDMA-based user scheduling and the dynamic UAV trajectory to maximize the system throughput. The formulated problem is non-convex which makes it difficult to solve directly, hence we propose an iterative algorithm to obtain an approximate optimal solution based on block coordinate descent and successive convex optimization techniques. Simulation results demonstrate that our proposed TDMA-based protocol outperforms the OFDMA-based ones with fixed UAV position/trajectory when the UAV helps relay information for multiple source-destination pairs.

Abstract The electromagnetic situation, which can promote the abilities of understanding and decision‐making for the battlefield, has attracted significant interest recently in information‐based warfare. This paper investigates the deep learning‐based electromagnetic situation analysis and judgment in a complicated battlefield environment. To comprehensively simulate the two‐sided battling process, a turn‐based confrontation strategy is proposed, and an electromagnetic situation analysis and judgment model are then designed based on the AlphaGo Zero algorithm to achieve efficient situation analysis and decision‐making. In addition, an electromagnetic situation‐based attack‐defense platform is developed to realize and evaluate this designed model. Simulation results demonstrate that this designed model achieves significant performance in electromagnetic situation analysis and judgment compared with the Monte Carlo Tree Search based baseline.

Abstract With the explosive growth of data communications, existing infrastructure networks are under ever‐increasing pressure. Due to the advantages of fully controllable mobility, rapid deployment, and low cost, the unmanned aerial vehicles (UAVs) have attracted much attentions from both industry and academia in recent years, and it has become an inevitable trend to employ UAVs to enhance the network performance in different environments. As an important paradigm of UAV‐assisted communications, UAV relaying communications has been regarded as a promising solution in enhancing connectivity and improving transmission rate. This paper for the first time comprehensively summarizes UAV relaying communications and its application scenarios, including single UAV relaying networks, multi‐user UAV relaying networks, multi‐hop UAV relaying networks, as well as Internet of UAVs, and deeply analyzes the key technologies and challenges to be solved under this topic. Furthermore, the state‐of‐the‐art researches and opportunities of UAV relaying communications are discussed in detail.

Abstract Nowadays, driving safety has become an important topic in the field of intelligent driving. The key step in the intelligent transportation system (ITS) is to detect and track the vehicles on the road through various sensors equipped on the vehicle. While putting the sensors aside the road can have benefits in the vehicle‐road collaborative scenario. In this paper, an efficient and applicable multi‐sensor fusion protocol for a vehicle‐road collaborative system is proposed where roadside units (RSUs) are equipped with cameras and radars. To fully use the history information and the multi‐source heterogeneous sensing data to improve the reliability of multi‐sensor fusion, a Bayesian‐based two‐layer fusion scheme is further proposed. Moreover, the efficiency and robustness of the proposed scheme are evaluated in a real highway environment in Changsha, China.

Unmanned aerial vehicles (UAVs) facilitate information collection greatly in the Internet-of-Things (IoT) systems due to their superior flexibility and mobility. On the other hand, nonorthogonal multiple access (NOMA) is regarded as a promising technology to provide high spectral efficiency and support massive connectivity in fifth-generation networks. The integration of NOMA into UAV-assisted wireless networks shows great potential, but how to determine the user grouping and power allocation in NOMA according to the high mobility of UAV is challenging. In this article, we propose a general NOMA-enabled UAV-assisted data collection (NUDC) protocol to maximize the sum rate of a wireless sensor network (WSN), where the location of UAV, sensor grouping, and power control are jointly considered. Moreover, a joint signal-to-interference ratio (SIR) hypergraph-based grouping and power control (SHG-PC) NOMA scheme is provided to obtain the appropriate sensor grouping and the optimal power control solutions efficiently, in which the hypergraph and the greedy coloring algorithm are exploited to find out the optimized group relationships. Extensive simulation results demonstrate the efficiency of our proposed protocol.

Based on the advantages of small size, lightweight, as well as flexible deployment and recycling, unmanned aerial vehicle (UAV) has been more and more widely used in military and civilian. As flying relays, UAVs can quickly set up relay communication links for different missions, to enhance the receiving signal power, increase the system capacity, and expand the communication coverage. In this article, we investigate full-duplex (FD) UAV relaying for multiple source-destination pairs. To fully exploit the flying flexibility of the UAV in serving multiple source-destination pairs, we propose a scheduling protocol that exploits time-division multiple access (TDMA) to serve different source-destination pairs in turns when flying along an optimized trajectory. Then, we further formulate a joint optimization problem of the TDMA-based user scheduling, the dynamic UAV trajectory, and the UAV transmit power to maximize the system throughput. The formulated problem is nonconvex that makes it difficult to solve directly, hence we propose an iterative algorithm to obtain an approximate optimal solution based on block coordinate descent and successive convex optimization techniques. Simulation results demonstrate that our proposed FD-based UAV relaying network achieves significant throughput gains compared with the half-duplex (HD) baseline, and the TDMA-based protocol outperforms the OFDMA-based ones with fixed UAV position/trajectory when the UAV helps relay information for multiple source-destination pairs.

Non-orthogonal multiple access (NOMA) is regarded as a promising technology to provide high spectral efficiency and support massive connectivity in 5G systems. In most existing NOMA user grouping approaches, users are grouped into disjoint groups, which may lead to a waste of power resources within each NOMA group. Motivated by this, in this paper we propose a novel generalized user grouping (GuG) concept for NOMA from an overlapping perspective, which allows each user to participate in multiple groups but subject to individual maximum power constraint. In order to achieve effective GuG and maximize the system sum rate, we formulate a joint power control and GuG optimization problem. Then, we address this problem by exploiting the overlapping coalition formation (OCF) game framework, and we further propose an OCF-based algorithm in which each user can be self-organized into a desirable overlapping coalition structure. Simulation results verify the efficiency of GuG in NOMA systems and indicate that compared with traditional NOMA user grouping schemes, our proposed OCF-based GuG NOMA scheme achieves significant performance gains in terms of system sum rate.

In Vehicle-to-Everything (V2X) networks, where all vehicles and infrastructures are interconnected for information sharing, data dissemination is increasingly playing a significant role in superior and pluralistic communication services. To empower the efficiency of data dissemination, in this paper, we propose a novel unmanned aerial vehicle (UAV)-enabled scheduling protocol consisting of a proactive caching policy and a file sharing strategy in V2X networks. In the proactive caching process, we deploy UAVs as flying base stations (BSs) with caching capability, where we propose a UAV dynamic trajectory scheduling (DTS) algorithm to optimize the caching duration. Whereas in the file sharing strategy, based on the previous vehicular caching status, we provide a framework of file sharing cycle for data dissemination scheduling and employ a channel prediction algorithm to alleviate communication overhead. Moreover, we propose a relay ordering algorithm to effectively improve the file sharing process. Simulation results demonstrate that, our proposed scheduling protocol can enhance the efficiency of data dissemination and achieve an improved network performance in terms of caching process, system throughput, and file sharing latency in V2X networks.

Non-orthogonal multiple access (NOMA) is regarded as a promising technology to provide high spectral efficiency and support massive connectivity in 5G systems. Traditionally, NOMA user grouping is non-overlapping, leading to a waste of power resources within each NOMA group. Motivated by this, in this paper we propose a novel generalized user grouping (GuG) concept for NOMA from an overlapping perspective, which allows each user to participate in multiple user groups but subject to individual maximum power constraint. In order to achieve effective GuG and maximize the system sum rate, we formulate a joint power control and GuG optimization problem. Then we further provide a machine learning-based GuG scheme to obtain the optimized feasible GuG and the optimal power control solutions efficiently, in which the established machine learning-based model is exploited to explore the relative relationships of channel gains of users and obtain several fixed grouping patterns via Merge operation. Simulation results verify the efficiency of GuG in NOMA systems and indicate that compared with traditional NOMA user grouping schemes, our proposed GuG scheme achieves significant performance gains in terms of system sum rate.

Non-orthogonal multiple access (NOMA) is regarded as a promising technology to provide high spectral efficiency and support massive connectivity in 5G systems. In most existing NOMA user grouping approaches, users are grouped into disjoint groups, which may lead to a waste of power resources within each NOMA group. Motivated by this, in this paper we propose a novel generalized user grouping (GuG) concept for NOMA from an overlapping perspective, which allows each user to participate in multiple groups but subject to individual maximum power constraint. In order to achieve effective GuG and maximize the system sum rate, we formulate a joint power control and GuG optimization problem. Then, we address this problem by exploiting the overlapping coalition formation (OCF) game framework, and we further propose an OCF-based algorithm in which each user can be self-organized into a desirable overlapping coalition structure. Simulation results verify the efficiency of GuG in NOMA systems and show that our proposed OCF-based GuG NOMA scheme achieves significant performance gains in terms of system sum rate.

Non-orthogonal multiple access (NOMA) provides high spectral efficiency and supports massive connectivity in 5G systems. Traditionally, NOMA user grouping is non-overlapping, leading to a waste of power resources within each NOMA group. Motivated by this, we propose a novel generalized user grouping (GuG) concept for NOMA from an overlapping perspective, which allows each user to participate in multiple user groups but subject to individual maximum power constraint. We formulate a joint power control and GuG optimization problem, and then provide a machine learning-based GuG scheme to obtain the optimized feasible GuG and the optimal power control solutions efficiently. Simulation results show significant performance gains in terms of system sum rate.

In recent years, massive multiple-input multiple-output (MIMO) has been regarded as a promising technique in the fifth-generation (5G) communication systems. With the ability of focusing transmission beams on users, massive MIMO has a natural advantage in the field of physical layer security to improve the system secrecy performance. However, in practical mobile systems, the imperfect channel state information (CSI) caused by the channel estimation error and the transmission and processing delay will have a non-negligible impact on the system performance. In this paper, we investigate secure communications in a multi-user massive MIMO-enabled vehicular communication networks. Considering the influence of imperfect CSI on the secrecy performance, we derive a tight asymptotic lower bound of the system secrecy capacity under both perfect and imperfect CSI. Moreover, we further analyze the impact of vehicle speed on the system secrecy performance and propose a channel prediction scheme based on (Long Short-Term Memory) LSTM model to compensate for the negative effects of imperfect CSI, which can improve the system secrecy performance in high mobility scenario. Simulation results show that the imperfect CSI severely reduces the system secrecy capacity, but its negative effects can be effectively alleviated through the designed LSTM-based channel prediction and compensation scheme.

As one of the fundamental tasks in computer vision, semantic segmentation plays an important role in real world applications. Although numerous deep learning models have made notable progress on several mainstream datasets with the rapid development of convolutional networks, they still encounter various challenges in practical scenarios. Unsupervised adaptive semantic segmentation aims to obtain a robust classifier trained with source domain data, which is able to maintain stable performance when deployed to a target domain with different data distribution. In this paper, we propose an innovative progressive feature refinement framework, along with domain adversarial learning to boost the transferability of segmentation networks. Specifically, we firstly align the multi-stage intermediate feature maps of source and target domain images, and then a domain classifier is adopted to discriminate the segmentation output. As a result, the segmentation models trained with source domain images can be transferred to a target domain without significant performance degradation. Experimental results verify the efficiency of our proposed method compared with state-of-the-art methods.

Semantic segmentation is an essential task in plenty of real-life applications such as virtual reality, video analysis, autonomous driving, etc. Recent advancements in fundamental vision-based tasks ranging from image classification to semantic segmentation have demonstrated deep learning-based models' high capability in learning complicated representation on large datasets. Nevertheless, manually labeling semantic segmentation dataset with pixel-level annotation is extremely labor-intensive. To address this problem, we propose a novel multi-level feature alignment framework for cross-domain semantic segmentation of urban scenes by exploiting generative adversarial networks. In the proposed multi-level feature alignment method, we first translate images from one domain to another one. Then the discriminative feature representations extracted by the deep neural network are concatenated, followed by domain adversarial learning to make the intermediate feature distribution of the target domain images close to those in the source domain. With these domain adaptation techniques, models trained with images in the source domain where the labels are easy to acquire can be deployed to the target domain where the labels are scarce. Experimental evaluations on various mainstream benchmarks confirm the effectiveness as well as robustness of our approach.

Unmanned aerial vehicles (UAVs) facilitate information collection greatly in Internet of Things (IoT) systems. On the other hand, non-orthogonal multiple access (NOMA) is regarded as a promising technology to provide high spectral efficiency and support massive connectivity in 5G networks. The integration of NOMA into UAV-assisted wireless networks shows great potential, but how to determine the user grouping and power allocation in NOMA according to the different locations of UAV is challenging. In this paper, we propose a general NOMA-enabled UAV-assisted data collection (NUDC) protocol to solve the formulated sum rate maximization problem such that the location of UAV, sensor grouping, and power control are jointly considered. Moreover, a joint signal-to-interference-ratio (SIR) hypergraph-based grouping and power control (SHG-PC) NOMA scheme is provided to obtain the appropriate sensor grouping and the optimal power control solutions efficiently. Extensive simulation results demonstrate the effectiveness of our proposed protocol.

Unmanned aerial vehicle (UAV)-assisted communications are expected to become an important part of the next generation mobile communication systems, due to the high mobility of the UAVs. Non-orthogonal multiple access (NOMA) is regarded as a rosy technology in the fifth generation (5G) mobile communication systems, since it can effectively improve the spectral efficiency. In this paper, we combine the advantages of the UAV-assisted communications and NOMA, and propose a device-to-device (D2D)-enhanced UAV-NOMA network architecture, in which D2D is introduced to increase the file dispatching efficiency. Resource reuse based on spatial reuse is also allowed to further improve the spectral efficiency. Then, we propose a graph-based file dispatching (GFD) protocol to control the interference and minimize the UAV-assisted file dispatching mission time. Simulation results verify the advantages of our proposed D2D-enhanced UAV-NOMA network architecture and the efficiency of our designed GFD protocol.

Most recently, vehicular fog computing (VFC) has been regarded as a novel and promising architecture to effectively reduce the computation time of various vehicular application tasks in Internet of vehicles (IoV). However, the high mobility of vehicles makes the topology of vehicular networks change fast, and thus it is a big challenge to coordinate vehicles for VFC in such a highly mobile scenario. In this paper, we investigate the joint task assignment and resource allocation optimization problem by taking the mobility effect into consideration in vehicular fog computing. Specifically, we formulate the joint optimization problem from a Min-Max perspective in order to reduce the overall task latency. Then we decompose the nonconvex problem into two sub-problems, i.e., one to one matching and bandwidth resource allocation, respectively. In addition, considering the relatively stable moving patterns of a vehicle in a short period, we further introduce the mobility prediction to design a mobility prediction-based scheme to obtain a better solution. Simulation results verify the efficiency of our proposed mobility prediction-based scheme in reducing the overall task completion latency in VFC.

Unmanned aerial vehicle (UAV)‐assisted communications have been regarded as a promising technology, which can be used in dynamic heterogeneous networks, since the UAVs can be applied as mobile base stations (MBSs). It is more efficient to set the UAVs as MBSs in different layers according to their functions. Due to the ability in communicating with other vehicles or facilities, vehicle‐to‐everything (V2X) makes the information required by the vehicles be efficiently transmitted and processed, so as to increase the safety of driving. However, because of the rapid change of topology and the huge demand of data transmission, it is hard to guarantee the efficient coverage of all the vehicles only by the support of terrestrial wireless communication networks. Considering this, this study combines UAV and V2X communications together, and proposes a multi‐layer aerial‐road vehicular (MLARV) architecture. The UAVs in the higher layer are in charge of overall monitoring, while the UAVs in the lower layer are responsible for hot‐spot area coverage. To solve the throughput maximisation problem, the authors further propose a density‐aware deployment (DAD) scheme with an iterative three‐dimensional matching resource allocation algorithm. Simulation results show that the proposed DAD scheme with the MLARV architecture outperforms the traditional UAV‐assisted V2X communications with single layer architecture.

Underwater optical wireless sensor networks (UOWSNs) have been attracting many interests for the advantages of high transmission rate, ultrawide bandwidth, and low latency. However, due to limited energy resources and highly dynamic topology caused by the water flow movement, it is challenging to provide a low-consumption and reliable routing in UOWSNs. To tackle this issue, in this article, we propose an efficient routing protocol based on multiagent reinforcement learning, termed as DMARL, for UOWSNs. The network is first modeled as a distributed multiagent system, and residual energy and link quality are considered into the routing protocol design to improve the adaptation to a dynamic environment and the support of prolonging network life. Additionally, two optimization strategies are proposed to accelerate the convergence of the reinforcement learning algorithm. On the basis, a reward mechanism is provided for the distributed system. The simulation results show that the DMARL-based routing protocol has low energy consumption and high packet delivery ratio (over 90%), and it is suitable for networks where the average number of neighbor nodes is less than 14.

As a newly emerging communication assistant equipment, unmanned aerial vehicles (UAVs) can be exploited to dispatch data files quickly to specific areas and support rapid deployment of communication links in complex terrain, which is of great significance for specific communication demands in disaster and remote areas. Nonorthogonal multiple access (NOMA), as a rosy technology in the fifth generation (5G) and future mobile communication systems, has been widely studied because of its ability in improving spectral efficiency and reducing transmission latency to enhance the overall Quality of Service (QoS) and meet the strict communication requirements. Based on these, in this article, we propose a device-to-device (D2D)-enhanced UAV-NOMA network architecture, in which D2D is introduced to increase the file dispatching efficiency. In our proposed D2D-enhanced UAV-NOMA network, the ground users (GUEs) that have already received file blocks (FBs) are allowed to reuse the time-frequency resources assigned to NOMA links to share their FBs with other GUEs, which significantly improves the efficiency of file dispatching. But this also leads to a complicated interference environment. In order to effectively manage the interference and minimize the UAV-assisted file dispatching mission time, we propose a graph-based file dispatching (GFD) protocol, in which the complicated joint optimization problem is decomposed to be solved efficiently and graph theory-based algorithms are proposed for resource allocation. The simulation results verify the advantages of our proposed D2D-enhanced UAV-NOMA network architecture and the efficiency of our designed GFD protocol in minimizing the total UAV-assisted file dispatching mission time.

In recent years, autonomous driving has attracted a vast amount of attention from both industry and academia, which has introduced large amounts of region-related data. In order to release the burden in core networks caused by the communication demands for such data, various in-vehicle storage (IVS) systems have been widely studied to bring contents closer to users. To cope with the mobility issue hindering the realization of IVS systems, in this paper, we propose a relay selection strategy (RSS) consisting of the relay map construction (RMC) algorithm and the relay pair matching (RPM) algorithm with the assistance of the vehicle route information. By considering both the potential transmission amount and the waiting time, the proposed RSS generates an overall optimal relay assignment for the IVS system. The performance gain of the proposed RSS compared with the baseline is evaluated by a realistic simulator in terms of the relay failure ratio, the retrieval throughput, and the RSU consumption ratio. Simulation results show that the proposed RSS achieves higher efficiency and robustness than the baseline.

Vehicle-to-Everything (V2X) communications refers to an intelligent and connected vehicular network where all vehicles and infrastructure systems are interconnected with each other. Data dissemination is playing an increasingly significant role in enhancing the network connectivity and data transmission performance. However, conventional scenarios and protocols cannot satisfy the growing pluralistic and superior quality of services (QoS) requirements of included vehicles. Therefore, in this paper, we propose a novel unmanned aerial vehicle (UAV)-assisted data dissemination protocol with proactive caching at the vehicles and an advanced file sharing strategy for revolutionizing communications. Specifically, in the proactive caching phase, we employ UAVs to act as flying base stations (BSs) for information interactions. Considering the time-variant network topology, we further propose a spatial scheduling (SS) algorithm for the trajectory optimization of each UAV, which can expedite the caching process and boost the system throughput. Then in the file sharing phase, based on the previous caching status, we provide a relay ordering algorithm to enhance the network transmission performance. Numerical results verify that our proposed UAV-assisted data transmission protocol can achieve a desirable system performance in terms of the downloading process, network throughput, and average data delivery delay.

Data dissemination is a promising application in vehicular ad-hoc networks (VANETs) to overcome the limitation in the connection time of specific vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) links, and provide efficient large data file transfer from road-side units (RSUs) to vehicles therein. Unmanned aerial vehicles (UAVs), recently regarded as an effective supplement in wireless networks, can provide line-of-sight (LoS) links with better channel quality, and their high flexibility and maneuverability are beneficial for on-demand deployment in communication systems. In this paper, by employing UAVs as flying relays with data caching capability in VANETs, we design an enhanced UAV-aided data dissemination protocol. Specifically, we propose a centralized UAV trajectory scheduling algorithm based dynamic programming (CTS-DP) to optimize the flying routes of UAVs. Then, based on the scheduled trajectories of UAVs, we further propose a centralized UAV-aided data dissemination scheduling strategy to achieve both effective and efficient coordination of the RSUs, UAVs, and vehicles for data dissemination. Numerical simulations in vehicular scenarios verify the efficiency of the proposed protocol with dynamic UAV trajectory scheduling in terms of downloading progress, data dissemination delay, and system throughput.

Data dissemination is playing a crucial role in improving the connectivity and performance in hybrid vehicular ad-hoc networks (VANETs) by exploiting both vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication links, which can achieve effective data sharing and distribution between road-side units (RSUs) and vehicles. Recently, unmanned aerial vehicles (UAVs), acting as flying base stations (BSs) or relays with caching capability, have been widely investigated as an effective and enhanced communication support from the sky to provide the ground users improved quality of services (QoS) in a variety of circumstances. In this paper, by fully exploiting the advantages of UAVs introduced in VANETs, we design an advanced UAV-aided cooperative data dissemination scheduling strategy to improve the data dissemination performance in VANETs. Considering the mobility of the involved UAVs, we further propose a three-dimensional (3D) spatial dynamic programming (SDP) algorithm for the trajectory scheduling of UAVs to optimize the network transmission utility. Simulations results verify that, compared with other data dissemination strategies, our proposed UAV-aided cooperative data dissemination strategy can efficiently achieve a better system performance in terms of downloading progress and transmission delay.

Vehicle-to-everything (V2X) communications are regarded as the key technology in future vehicular networks due to its ability in improving the traffic efficiency and safety, and reducing congestion. Recently, non-orthogonal multiple access (NOMA), as a promising solution in the fifth generation (5G) mobile communication systems, has drawn much attention because it can significantly improve the network throughput and lower the accessing and transmission latency to meet the requirements of many 5G-enabled applications. Noticing these, in this paper, we propose to introduce NOMA in device-to-device (D2D)-enhanced V2X networks, where D2D-enabled resource sharing based on spatial reuse for different V2X communication groups are permitted through centralized resource management. Such an enhanced NOMA-V2X architecture results in a more complicated and challenging interference scenario. In order to efficiently solve the interference management and resource allocation problem in the NOMA-V2X network, we construct a weighted 3-partite interference hypergraph to model the relationships among different communication groups. Then, based on the constructed hypergraph, we further propose an interference hypergraph-based 3-dimensional matching (IHG-3DM) resource allocation protocol with a greedy 3DM algorithm. Simulation results verify the efficiency of our proposed IHG-3DM resource allocation protocol for NOMA-V2X communications in improving the network throughput.

Broadband connectivity and mobile technology have been widely applied in the world. With these advanced technologies, the proliferation of smart devices and their applications by accessing mobile internet have come up with a giant leap forward, leading to the ever‐increasing scale and complexity of cellular networks. This presents imminent challenges to anomaly detection in cellular networks. In this study, the authors discuss challenges and current literature of anomaly detection for cellular networks to embrace the ‘big data’ era. First, they review the state‐of‐the‐art techniques in the area of anomaly detection in cellular networks. Then, the challenges are pinpointed for anomaly detection due to the cellular network big data. Finally, they introduce a big data analytic‐based anomaly detection method for cellular networks.

This study focuses on the spectral efficiency of massive multiple‐input–multiple‐output (MIMO) systems over Ricean fading channels. Assuming the channel reciprocity of uplink and downlink transmissions, the channels are estimated through uplink pilot sequence with least minimum mean square error (MMSE) estimator, which facilitates the investigation of equivalent channel model. With the equivalent channel model, adopting joint MMSE receiver, the uplilnk achievable sum‐rate is studied, and finally, the asymptotic expression of spectral efficiency is obtained. Based on the proposed asymptotic spectral efficiency, numerical results give some guidelines about system parameters design: the authors should choose a proper length of the block fading channels to reduce the effect of pilot overhead, and an optimal length of pilot sequence always exists to guarantee a satisfying system performance in terms of spectral efficiency. Furthermore, the simulation results show that when fixing the number of users, shrinking the cell serving area and increasing the number of antennas can improve the system performance.

Vehicular communication network is a core application scenario in the fifth generation (5G) mobile communication system which requires ultrahigh data rate and ultralow latency. Most recently, nonorthogonal multiple access (NOMA) has been regarded as a promising technique for future 5G systems due to its capability in significantly improving the spectral efficiency and reducing the data transmission latency. In this paper, we propose to introduce NOMA in device-to-device-enhanced vehicle-to-everything (V2X) networks, where resource sharing based on spatial reuse for different V2X communications are permitted through centralized resource management. Considering the complicated interference scenario caused by NOMA and spatial reuse-based resource sharing in the investigated NOMA-integrated V2X (NOMA-V2X) networks, we construct an interference hypergraph (IHG) to model the interference relationships among different communication groups. In addition, based on the constructed IHG, we further propose an IHG-based resource allocation (IHG-RA) scheme with cluster coloring algorithm, which can lead to both effective and efficient resource block assignment with low computational complexity. Simulation results verify the efficiency of our proposed IHG-RA scheme for NOMA-V2X communications in improving the network sum rate.

The current age is witnessing speedy revolution of vehicles from the hundred-year old moving metal box on four wheels into a new species with dazzling intelligence. To enable such intelligence, the nervous system heavily hinges upon the connectivity among vehicles as well as between vehicles and the transportation infrastructure. With such intelligence, humans would be relieved from the driving duties and naturally convert the vehicle into moving offices or entertainment rooms, thus imposing unprecedented burden to the connectivity to the world beyond the vehicle. Due to the mobile nature of vehicles, wireless naturally becomes the rescue. However, though wireless has been, to some extent, deployed on vehicles for more than half a century, the current wireless-vehicle interactions are, to the best, a mere combination, in which the wireless systems are designed accounting for the mobile environment, but do not have much to do with the vehicle core functions. In this paper, we will discuss the challenges, progresses and perspectives of the present-to-the-near-future vehicular wireless channels, wireless-vehicle combination, as well as the more demanding wireless-vehicle integration.

Collisions caused by the hidden terminal effects may result in severe packet corruption and performance degradation in multihop IEEE 802.15.4-compliant wireless sensor networks (WSNs). In order to avoid such collisions through scheduling protocols, it is important to first recognize these collisions by distinguishing them from some other noncollision cases (e.g., path loss, multipath fading, shadow fading, and IEEE 802.11 interference), which may also lead to similar consequences. In this paper, we focus on the collision recognition problem in multihop IEEE 802.15.4-compliant WSNs. First, through a series of measurements of the error properties in various collision and noncollision scenarios, we investigate the statistical behaviors of error patterns including the bit error rate and error position distribution, which reveal obvious differences between collision and noncollision cases in terms of bit- and symbol-level error position distribution. Based on these observations, we further propose a machine learning-based collision recognition mechanism by inserting some redundant blocks in a data frame. The inserted blocks are known to both the sender and receiver, thereby it enables the receiver to recognize the error patterns only according to the redundant sequences. Moreover, a mutual information-guided byte selection technique is also provided to effectively improve the recognition accuracy. Finally, the proposed mechanism is verified under three different transmission environments. The experimental results show that the proposed mechanism achieves good recognition accuracy over 90% with 94% coding efficiency.

In recent years, physical layer security has been regarded as a promising technique to facilitate secure communications in next generation mobile systems, where theoretically massive MIMO can significantly enhance the system secrecy performance under its advantage in shaping the transmitted signals to null the interference or leakage. However, in practical systems, the achievable secrecy performance under imperfect channel state information (CSI) deserves further investigation. In this paper, we give a detailed analysis about the physical layer security problem in a multi-user massive MIMO system with imperfect CSI. The considered imperfect CSI includes both the outdated CSI due to the transmission and processing delay, and the channel estimation error. We first derive a tight asymptotic lower bound of the ergodic system secrecy capacity under imperfect CSI, and then analyze how imperfect CSI affects the system secrecy performance. Moreover, we propose a channel prediction scheme that can result in more accurate CSI, in order to alleviate the negative effect on the achievable system secrecy capacity caused by imperfect CSI. Simulation results reveal that the imperfect CSI greatly reduces the system secrecy capacity, while our designed channel prediction scheme can effectively mitigate the harmful impact of imperfect CSI and thus improve the system secrecy performance.

In this paper, we investigate flexible power transfer among electric vehicles (EVs) from a cooperative perspective in an EV system. First, the concept of cooperative EV-to-EV (V2V) charging is introduced, which enables active cooperation via charging/discharging operations between EVs as energy consumers and EVs as energy providers. Then, based on the cooperative V2V charging concept, a flexible energy management protocol with different V2V matching algorithms is proposed, which can help the EVs achieve more flexible and smarter charging/discharging behaviors. In the proposed energy management protocol, we define the utilities of the EVs based on the cost and profit through cooperative V2V charging and employ the bipartite graph to model the charging/discharging cooperation between EVs as energy consumers and EVs as energy providers. Based on the constructed bipartite graph, a max-weight V2V matching algorithm is proposed in order to optimize the network social welfare. Moreover, taking individual rationality into consideration, we further introduce the stable matching concepts and propose two stable V2V matching algorithms, which can yield the EV-consumer-optimal and EV-provider-optimal stable V2V matchings, respectively. Simulation results verify the efficiency of our proposed cooperative V2V charging-based energy management protocol in improving the EV utilities and the network social welfare as well as reducing the energy consumption of the EVs.

Vehicular communication network is a core application scenario in the fifth generation (5G) mobile communication system which requires ultra high data rate and ultra low latency. Most recently, non-orthogonal multiple access (NOMA) has been regarded as a promising technique for future 5G systems due to its capability in significantly improving the spectral efficiency and reducing the data transmission latency. In this paper, we propose to introduce NOMA in D2D-enabled V2X networks, where resource sharing based on spatial reuse for different V2X communications are permitted through centralized resource management. Considering the complicated interference scenario caused by NOMA and spatial reuse-based resource sharing in the investigated NOMA-integrated V2X networks, we construct an interference hypergraph to model the interference relationships among different communication groups. In addition, based on the constructed hypergraph, we further propose an interference hypergraph-based resource allocation (IHG-RA) scheme with cluster coloring algorithm, which can lead to both effective and efficient QoS-guaranteed resource block (RB) assignment with low computational complexity. Simulation results verify the efficiency of our proposed IHG-RA scheme for NOMA-integrated V2X communications in improving the network sum rate.

In recent years, physical layer security has been regarded as a promising concept to provide secure communications in next generation mobile systems, where theoretically massive MIMO can significantly enhance the system secrecy performance due to its advantage in shaping the transmitted signals to null the interference or leakage. However, in practical systems, the channel state information is often imperfect due to the outdated channel effect and the channel estimation error. In this paper, we investigate the physical layer security problem in a multi-user massive MIMO system under imperfect CSI. We first derive a tight asymptotic lower bound of the ergodic system secrecy capacity under imperfect CSI, and then analyze how imperfect CSI affects the system secrecy performance. Simulation results reveal the negative impact of the imperfect CSI on the secrecy performance of massive MIMO systems and the accuracy of our theoretical derivations and analysis.

In high-speed vehicular ad-hoc networks (VANETs), cooperative data dissemination is an effective solution to amend the limited connection time of communication links between roadside units (RSUs) and vehicles. Existing data dissemination strategies utilize efficient cooperation of vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication links to maintain the data transmissions and thus improve the system performance. Recently, unmanned aerial vehicle (UAV) is widely utilized in communication systems, which has a high probability of line-of-sight (LoS) links with better channel quality and can be dynamically deployed. In this paper, we propose a novel UAV-assisted data dissemination scheduling strategy in VANETs. The recursive least squares (RLS) algorithm is utilized to predict the vehicle mobility with low complexity and high prediction accuracy. To enhance the transmission utilities of the UAVs, we further propose a maximum vehicle coverage (MVC) algorithm to schedule the two-dimensional (2D) movements of the UAVs during the process of data dissemination. Simulations in both urban and highway scenarios verify that the proposed UAV-assisted data dissemination strategy achieves a significant reduction of data dissemination delay and an improvement of system throughput.

Distributed storage systems (DSSs) are widely employed in data centers and sensing networks to resist storage node failures. Structured redundancy is introduced to DSS by various coding schemes to efficiently account for failures of storage nodes. The allocation of the coded data blocks to storage nodes is another factor that impacts the data reliability. In this paper, we investigate the coding and allocation problem on multiple data objects in DSS. We propose a cross-object coding and allocation (COCA), which amounts to encoding and symmetric allocation on one large virtual data object aggregated by multiple data objects. We first explore the benefits of the proposed COCA scheme and find its reliability improvement in terms of joint successful recovery probability. However, such reliability improvement comes at the cost of increased data retrieval complexity. Hence, an optimization problem is formulated to explore the tradeoff between data reliability and data retrieval complexity. By employing a coalition formation game to model the process of the data objects grouping, we also propose a coalition-formation-based grouping algorithm to provide a suboptimal solution with greatly reduced computation complexity. Simulations validate the reliability improvement of our proposed COCA scheme and the effectiveness of our proposed coalition-formation-based algorithm.

In this paper, we investigate the physical layer security issue in a cellular network with a full-duplex (FD) base station and multiple uplinks and downlinks. We provide a novel cooperative jamming mechanism in order to enhance the secrecy performance for the investigated scenario. The total bandwidth is divided into multiple resource blocks (RBs). In our investigation, each uplink or downlink can acquire at most one RB, and each RB can only be assigned to one uplink and one downlink. We formulate the secrecy capacity maximization problem as a joint RB assignment and power allocation problem, and then propose a bipartite graph based propose-and-decide algorithm (BGPDA) to solve this problem effectively and efficiently. The numerical simulations verify the efficiency of our proposed algorithm.

In this paper, we investigate the relay selection (RS) problem in power splitting (PS) based energy-harvesting (EH) half-duplex (HD) relay networks, where the relays are wirelessly powered by harvesting a portion of the received RF signal power. We expand the relay selection problem in PSEH-HD relay networks to allow multiple relays to cooperate simultaneously. Furthermore, the optimal PS factor is obtained in closed form to facilitate the RS. Simulations show that neither the single RS nor the all-participate RS is optimal across all SNR levels. To improve the capacity of the network by better exploiting the cooperative diversity, a heuristic RS strategy with quadratic complexity is then proposed. The performance of our proposed updated relay ordering based relay selection (URO-RS) strategy is evaluated by simulations, which achieves near-optimum performance in comparison with the exhaustive search based RS strategy but with significantly reduced complexity.

This paper proposes a resource allocation policy which enhances physical layer security in vehicular communication networks. Due to the high mobility in vehicular networks, the feedback channel state information (CSI) can easily get outdated, especially when it takes non-negligible time to obtain the resource-allocation solution. Under the assumption that only outdated CSI is available, we formulate the problem as joint power and subcarrier allocation in order to optimize the uses' secrecy rate based on maximum-minimum (max-min) fairness criterion. The formulated optimization problem is a mixed integer nonlinear programming problem. To reduce the complexity, we further propose a two-step suboptimal algorithm that performs power and subcarrier allocation separately. For a given subcarrier assignment, the optimal power allocation is solved by developing an algorithm of polynomial computational complexity. Numerical results show that the performance of our proposed algorithm can approximate to the optimal one.

In this paper, we investigate the flexible and efficient charging mechanism for electric vehicles (EVs). We first provide a developed V2V charging concept, termed as cooperative V2V charging, which enables active cooperation through charging and discharging operations between EVs as energy consumers and EVs as energy providers and is beneficial to both sides. Then, based on the defined utilities of EVs as energy consumers and EVs as energy providers, we propose a novel stable matching based cooperative V2V charging mechanism by taking each EV's individual rationality into consideration. Furthermore, we provide two efficient stable V2V matching algorithms, resulting in optimal V2V matching solutions in terms of the utilities of EVs as energy consumers and the utilities of EVs as energy providers, respectively. Simulation results verify the efficiency of our proposed stable matching based cooperative V2V charging mechanism in improving the utilities of both EVs as energy consumers and EVs as energy providers as well as reducing the energy consumption of the EVs compared with the traditional EV charging protocol.

Vehicular ad-hoc networks (VANETs), as an important component of intelligent transportation systems (ITS), have been attracting more and more research interests for their various promising applications. Although various MAC protocols have been proposed, efficient medium access remains a significant challenge in VANETs, especially in improving the network throughput in heavy traffic vehicular networks. In this paper, we propose an interference-free graph based time-division multiple access (IG-TDMA) protocol for VANETs. In the proposed protocol, roadside units (RSUs), as centralized controllers, collect the information from active vehicles and construct the interference-free graph based on the vehicle locations and a preset interference-free threshold. We further propose a communication link selection algorithm, which can help the RSUs make efficient and effective scheduling decisions with high spatial reuse efficiency and low computational complexity. Simulations verify that the proposed IG-TDMA protocol can improve the network performance significantly compared with the IEEE 802.11p CSMA/CA based EDCA scheme and traditional TDMA protocol.

To cope with the growing trend of asymmetric data traffic, the bidirectional dynamic networks (BDNs) dynamically allocate the number of uplink and downlink remote radio heads (RRHs), which facilitates simultaneous uplink and downlink communications. In this study, the authors derive the asymptotic approximations of the achievable uplink and downlink rates using maximum ratio transmission precoder and maximum ratio combination receiver, as the RRH antenna number ( M ) approaches infinity. Considering an optical fibre connected backhaul network, a practical power consumption model is presented to study the system energy efficiency (EE). Based on the asymptotic analysis, they exploit the power scaling laws that both the uplink and downlink powers should scale down to 1/ M to maintain a desirable uplink or downlink rate. Numerical results verify that when M is large, the BDN system outperforms the dynamic time division duplex system in both the spectral efficiency and EE.

Cooperative localization has emerged as a promising technique which can complement or even replace global positioning systems (GPS) in many practical scenarios, such as GPS-denied environments. In this paper, we concentrate on the distributed cooperative localization design. The conventional distributed cooperative localization approaches usually yield high computational complexity and communication overhead, due to the lack of an efficient link selection mechanism. For this purpose, we propose a novel concept named opportunistic cooperative localization, based on which each agent is able to select the most informative links rather than utilize all the possible links in a distributed, self-organized, and self-optimized manner. To achieve effective opportunistic selection, overlapping coalition formation (OCF) game is employed. In addition, we also provide an optimized terminating criterion, based on which the agents will be able to know whether and when they are well localized, and thus can terminate their localization procedure efficiently. Through simulations, we observe that by virtue of the proposed OCF game based opportunistic cooperative localization scheme along with the provided terminating criterion, the limitations of conventional distributed cooperative localization can be alleviated at the cost of negligible performance degradation.

In this paper, we investigate the optimization of the power splitting factor and the relay selection problem in two-way full-duplex (FD) relay networks, where the relays are wirelessly powered by harvesting a portion of the received signal power from the sources. To the best of the authors' knowledge, this is the first time that the two-way FD relays with simultaneous wireless and information transfer (SWIPT) capabilities are investigated. For each relay, we prove the quasi- convexity of the power splitting (PS) factor optimization and obtain the optimal PS factor in terms of the outage probability by linear search. We propose two relay selection schemes that minimize the outage probability and maximize the sum capacity respectively. The performance improvement over the random selection scheme and the all- participate scheme is demonstrated by simulations.

This paper investigates the performance of bidirectional dynamic networks (BDN) with massive multiple input multiple output (MIMO) systems. In BDN, dynamic allocation of the number of uplink and downlink remote radio heads (RRHs) is proposed, which offers a flexible solution to balance the data traffic asymmetry without requiring the time synchronization. Intuitively, the interference between the downlink and uplink RRHs is one of the main challenges in BDN. However, we prove that the massive MIMO strategy can effectively reduce a certain portion of the downlink-to-uplink interference. We derive the approximations of the achievable uplink and downlink rates using a maximum ratio transmission (MRT) precoder and a maximum ratio combination (MRC) receiver. Based on the asymptotic analysis, we exploit the power scaling laws that both the uplink and downlink power should scale down to 1/M (M is the antenna number) to ensure a desirable uplink or downlink rate. Furthermore, simulations show that BDN outperforms traditional time division duplex (TDD) systems in terms of the spectral efficiency.

In this paper, we investigate the flexible power transfer among electric vehicles (EVs) from a cooperation perspective in an energy Internet based EV system. First, we introduce the concept of cooperative EV-to-EV (V2V) charging, which enables active cooperation via charging/discharging operations between EVs as energy consumers and EVs as energy providers. Then, based on the cooperative V2V charging concept, we propose a flexible energy management protocol, which can help the EVs achieve more flexible and smarter charging/discharging behaviors. In the proposed energy management protocol, we define the utilities of the EVs based on the cost and profit through cooperative V2V charging and employ the bipartite graph to model the charging/discharging cooperation between EVs as energy consumers and EVs as energy providers. Based on the constructed bipartite graph, we propose a max-weight V2V matching algorithm, which can lead to an optimized V2V matching in terms of the network social welfare. Simulation results verify the efficiency of our proposed cooperative V2V charging based energy management protocol in improving the EV utilities and the network social welfare as well as reducing the energy consumption of the EVs.

In this paper, we investigate the physical layer security issue in Device-to-Device (D2D) communications underlaying cellular networks. In order to optimize the system secrecy rate of the cellular secure communication, we derive the optimal joint power control solutions of both the cellular communication links and D2D pairs in terms of the secrecy capacity. Furthermore, we propose a secrecy-based joint power and access control (JPAC) scheme with optimum D2D pair selection mechanism that can achieve an improved network secrecy performance with very low computational complexity. Simulation results validate the efficiency of the proposed secrecy-based JPAC scheme.

In this paper, we investigate the cooperation issue via spectrum sharing when employing physical layer security concept into the device-to-device (D2D) communications underlaying cellular networks. First, we derive the optimal joint power control solutions of the cellular communication links and D2D pairs in terms of the secrecy capacity under a simple cooperation case and further propose a secrecy-based access control scheme with the best D2D pair selection mechanism. Then, we consider a more general case that multiple D2D pairs can access the same resource block (RB) and one D2D pair is also permitted to access multiple RBs, and provide a novel cooperation mechanism in the investigated network. Furthermore, we formulate the provided cooperation mechanism among cellular communication links and D2D pairs as a coalitional game. Then, based on a newly defined max-coalition order in the constructed game, we further propose a merge-and-split-based coalition formation algorithm for cellular communication links and D2D pairs to achieve efficient and effective cooperation, leading to improved system secrecy rate and social welfare. Simulation results indicate the efficiency of the proposed secrecy-based access control scheme and the proposed merge-and-split-based coalition formation algorithm.

In this paper, we investigate the cooperation issue via spectrum sharing when employing the physical layer security concept into the Device-to-Device (D2D) communications underlaying cellular network. Different from previously related works, we consider a more general interference case that multiple D2D pairs can access the same resource block (RB) and one D2D pair is also permitted to access multiple RBs, and provide a novel cooperation mechanism in the investigated D2D communications underlaying cellular network. Furthermore, we formulate the provided cooperation mechanism among cellular communication links and D2D pairs as a coalitional game. Then, based on a newly defined Max-Coalition order in the constructed game, we further propose a merge-and-split based coalition formation algorithm for cellular communication links and D2D pairs to achieve efficient and effective cooperation, leading to both improved system secrecy rate and social welfare. Simulation results indicate the efficiency of the designed cooperation mechanism and the proposed merge-and-split based coalition formation algorithm.

In this paper, we focus on the downlink (DL) and uplink (UL) power control issue in a wireless full-duplex system consisting of a full-duplex base station with single antenna and mobile stations working in time-division duplex mode. First, we investigate the signal-to-interference ratios (SIRs) of DL and UL and obtain a formula reflecting their relationship. Then, by employing the Lagrange multiplier method, we derive an optimal joint power control solution to maximize the sum rate of DL and UL channels. Consequently, we propose an efficient switching scheme between the full-duplex mode and the opportunistic half-duplex mode that can be considered as a compensation mode in sum rate maximization. The proposed switching scheme is verified by numerical simulations.

In this paper, we propose a novel centralized time-division multiple access (TDMA)-based scheduling protocol for practical vehicular networks based on a new weight-factor-based scheduler. A roadside unit (RSU), as a centralized controller, collects the channel state information and the individual information of the communication links within its communication coverage, and it calculates their respective scheduling weight factors, based on which scheduling decisions are made by the RSU. Our proposed scheduling weight factor mainly consists of three parts, i.e., the channel quality factor, the speed factor, and the access category factor. In addition, a resource-reusing mode among multiple vehicle-to-vehicle (V2V) links is permitted if the distances between every two central vehicles of these V2V links are larger than a predefined interference interval. Compared with the existing medium-access-control protocols in vehicular networks, the proposed centralized TDMA-based scheduling protocol can significantly improve the network throughput and can be easily incorporated into practical vehicular networks.

Recently, localization has become an indispensable technique for wireless applications. In view of the limitation of global position system (GPS) in certain environments, alternative approaches are in demand. In this paper, we consider a cooperative localization approach named sum-product algorithm over a wireless network (SPAWN). Although SPAWN theoretically facilitates cooperative localization, it has several practical limitations. Specifically, SPAWN results in high computational complexity and increased network traffic. The main complexity of SPAWN lies in the selection of agents/anchors involved in the cooperative localization. To this end, we formulate the agent/anchor selection problem into a network formation game. Together with a practical limit on the number of agents/anchors used for cooperative localization, our proposed approach can markedly reduce the computational complexity and the resultant network traffic. Simulations show that these advantages come with a slight degradation in the localization mean squared error (MSE) performance.

In this study, a two‐stage decode‐and‐forward cooperative network is investigated consisting of a source, a corresponding destination, an eavesdropper and several intermediate nodes. In this study, the authors present an analysis of overall secrecy rate considering both distributed relay selection and secure beamforming problems. The achievable rate constraint is newly included into the system model. This modelling framework offers a more reliable approximation of the practical wireless channel. The relay selection is formulated as a coalitional game with transferable utility, which decreases the computation complexity in solving the distributed relay selection problem. A new ‘Max–Pareto order’ is constructed, which not only considers the player value, but also reflects the dominated weight of coalition value. Moreover, a distributed merge‐and‐split coalition formation algorithm is presented in this study. This algorithm achieves the system performance close to the theoretical upper limit, but it requires much less computation consumption.

Data dissemination is a promising application for the vehicular network. Existing data dissemination schemes are generally built upon some random-access protocol, which results in the unavoidable collision problem. To address this problem, in this paper we design a novel data dissemination strategy from the scheduling perspective. A data dissemination scheduling framework is then proposed. In the proposed framework, the main challenge is how best to assign the transmission opportunity to nodes with maximum dissemination utility and to avoid the collision problem. We then propose a novel and practical relay selection strategy and adopt the space-time network coding (STNC) with low detection complexity and space-time diversity gain to improve the dissemination efficiency. Compared with the random-access dissemination such as CodeOn-Basic and the noncooperative transmission, our proposed data dissemination strategy performs better in terms of the dissemination delay. In addition, the proposed strategy works even better in the dense network than the sparse scenario, benefitting from the space-time diversity gain of STNC and no-collision transmissions. This is in sharp contrary to the CodeOn-Basic method.

In this paper, we for the first time consider the space-time network coding (STNC) to propose a novel cooperative data dissemination strategy in the time-division multiple access (TDMA) manner for the vehicle network, to overcome the shortcomings of the traditional network coding (NC) techniques with carrier sense multiple access/collision avoidance (CSMA/CA) protocol, i.e, high signal detection complexity and channel collision problem. An optimal relay selection strategy in the procedure of cooperative data dissemination is proposed to maximize the average system capacity for the data dissemination. In a addition, a suboptimal relay selection strategy is put forward to reduce the computation complexity and thus makes the developed data dissemination strategy more practical. Compared with the non-cooperative data dissemination, the proposed cooperative data dissemination via STNC has better performance in terms of the average system capacity, the average user equipment (UE) capacity, and the average data dissemination delay.

This paper for the first time considers a low complexity multiple-input multiple-output (MIMO) mode switching technique between the conventional MIMO and distributed MIMO. By considering the conventional space-time block coding (STBC) and the distributed STBC (DSTBC), we propose a novel mode switching scheme between them to maximize the capacity performance. Since the difference between the STBC and D-STBC mainly comes from the three-directional (3D) distances, the proposed mode switching scheme is based on the jointly consideration of the received signal-to-noise ratio (SNR) at the user equipment (UE) and the 3D distances. Compared with non-adaptive MIMO modes, the proposed adaptive scheme has better capacity performance and thus is more energy-efficient.

In this paper, we investigate the resource sharing problem to optimize the system performance in device-to-device (D2D) communications underlaying cellular networks from a distributed and cooperative perspective. Specifically, we formulate a coalitional game with transferable utility, in which each user intends to maximize its own utility and has the incentive to cooperate with other users to form a strengthened user group that can increase the opportunity to win its preferred spectrum resources. Furthermore, we propose a distributed merge-and-split based coalition formation algorithm based on a new defined Max-Coalition order to effectively process the resource allocation problem. Simulation results confirm that, with much lower computational complexity, the proposed scheme achieves an approaching performance in terms of network sum-rate compared with the centralized optimal resource allocation scheme obtained via exhaustive search.

Device-to-device (D2D) communications underlaying cellular networks have recently been considered as a promising means to improve the resource utilization of the cellular network and the user throughput between devices in proximity to each other. In this paper, we investigate the resource sharing problem to optimize the system performance in such a scenario. Specifically, we formulate the interference relationships among different D2D communication links and cellular communication links as a novel interference-aware graph, and propose an interference-aware graph based resource sharing algorithm that can effectively obtain the near optimal resource assignment solutions at the base station (BS) but with low computational complexity. Simulation results confirm that, with markedly reduced complexity, our proposed scheme achieves a network sum rate that approaches the one corresponding to the optimal resource sharing scheme obtained via exhaustive search.

To ensure security in data transmission is one of the most important issues for wireless relay networks, and physical layer security is an attractive alternative solution to address this issue. In this paper, we consider a cooperative network, consisting of one source node, one destination node, one eavesdropper node, and a number of relay nodes. Specifically, the source may select several relays to help forward the signal to the corresponding destination to achieve the best security performance. However, the relays may have the incentive not to report their true private channel information in order to get more chances to be selected and gain more payoff from the source. We propose a Vickey-Clark-Grove (VCG) based mechanism and an Arrow-d'Aspremont-Gerard-Varet (AGV) based mechanism into the investigated relay network to solve this cheating problem. In these two different mechanisms, we design different "transfer payment" functions to the payoff of each selected relay and prove that each relay gets its maximum (expected) payoff when it truthfully reveals its private channel information to the source. And then, an optimal secrecy rate of the network can be achieved. After discussing and comparing the VCG and AGV mechanisms, we prove that the AGV mechanism can achieve all of the basic qualifications (incentive compatibility, individual rationality and budget balance) for our system. Moreover, we discuss the optimal quantity of relays that the source node should select. Simulation results verify efficiency and fairness of the VCG and AGV mechanisms, and consolidate these conclusions.

In this paper, we propose an interference-avoidance pilot design scheme using Zero-Correlation Zone (ZCZ) sequences for multi-cell MIMO-OFDM systems. In the proposed scheme, the ZCZ sequences from the same family set are designed as the time-domain (TD) pilot symbols and are Code-Division Multiplexing (CDM) for different transmit antennas in adjacent cells. Due to the perfect auto-correlation and cross-correlation properties of the ZCZ sequences within a certain correlation zone that is designed equal to or a little larger than the Cyclic Prefix (CP) length, the inter-cell interference of the pilot symbols can be effectively eliminated by utilizing a time-domain correlation-based channel estimation method. Simulation results show that the proposed scheme achieves near optimal normalized Mean Square Error (MSE) performance of channel estimation in a multi-cell environment.

In this paper, we investigate joint relay and jammer selection in two-way cooperative networks, consisting of two sources, a number of intermediate nodes, and one eavesdropper, with the constraints of physical-layer security. Specifically, the proposed algorithms select two or three intermediate nodes to enhance security against the malicious eavesdropper. The first selected node operates in the conventional relay mode and assists the sources to deliver their data to the corresponding destinations using an amplify-and-forward protocol. The second and third nodes are used in different communication phases as jammers in order to create intentional interference upon the malicious eavesdropper. First, we find that in a topology where the intermediate nodes are randomly and sparsely distributed, the proposed schemes with cooperative jamming outperform the conventional nonjamming schemes within a certain transmitted power regime. We also find that, in the scenario where the intermediate nodes gather as a close cluster, the jamming schemes may be less effective than their nonjamming counterparts. Therefore, we introduce a hybrid scheme to switch between jamming and nonjamming modes. Simulation results validate our theoretical analysis and show that the hybrid switching scheme further improves the secrecy rate.

In this paper, we investigate joint relay and jammer selection in two-way cooperative networks, consisting of two sources, a number of intermediate nodes, and one eavesdropper, with secrecy constraints. Specifically, the proposed algorithms select two or three intermediate nodes to enhance security against the malicious eavesdropper. The first selected node operates in the conventional relay mode and assists the sources to deliver their data to the corresponding destinations via the amplify-and-forward protocol. The second and third nodes are used in different communication phases as jammers in order to create intentional interference upon the eavesdropper node. Firstly, we find that in a topology where the relay and jamming nodes are randomly and sparsely distributed, the proposed schemes with cooperative jamming outperforms the conventional non-jamming schemes within a certain transmitted power regime. We also find that, in the scenario in which the intermediate nodes gather as a close cluster, the jamming schemes may be less effective than their non-jamming counterparts. Therefore, we introduce a hybrid scheme to switch between jamming and non-jamming modes. Simulation results validate our theoretical analysis that the hybrid switching scheme further improves the secrecy rate.

In this paper, we investigate cooperation of conventional relays and friendly jammers subject to secrecy constraints for cooperative networks consisting of one source node, one corresponding destination node, one malicious eavesdropper node, and several intermediate nodes. In order to obtain a higher secrecy rate, the source selects one conventional relay and several friendly jammers from the intermediate nodes to assist message transmission, and in return, it needs to make a payment. Each intermediate node here has two possible identities to choose, i.e., to be a conventional relay or a friendly jammer, which results in a direct impact on the final utility of the intermediate node. After the intermediate nodes determine their identities, they seek to find optimal partners forming coalitions, which improves their chances to be selected by the source and thus to obtain the payoffs in the end. We formulate this cooperation as a coalitional game with transferable utility and also study its properties. Furthermore, we define a Max-Pareto order for comparison of the coalition value, based on which we employ the merge-and-split rules. We also construct a distributed merge-and-split coalition formation algorithm for the defined coalition formation game. The simulation results verify the efficiency of the proposed coalition formation algorithm.

In this paper, we consider a two-way relay network where two sources can communicate only through an unauthenticated intermediate relay node. We investigate secure communications of this two-way relay scenario using physical layer security. Specifically, we treat the relay node as an eavesdropper from whom the information transmitted by the sources needs to be kept secret, despite the fact that its cooperation in relaying this information is essential. We first find that a non-zero secrecy rate is indeed achievable in this two-way relay network even without external jammers. Further still, with the help of friendly jammers who can transmit jamming signals to distract the malicious relay, a positive gain of the secrecy rate can be realized. In order to obtain the maximum secrecy rate, we define and then analyze an optimization problem. Finally, an optimal solution of jamming power allocation is provided for the system with friendly jammers.